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CN115616637A - A Navigation and Positioning Method for Urban Complex Environment Based on 3D Grid Multipath Modeling - Google Patents

A Navigation and Positioning Method for Urban Complex Environment Based on 3D Grid Multipath Modeling Download PDF

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CN115616637A
CN115616637A CN202211612232.3A CN202211612232A CN115616637A CN 115616637 A CN115616637 A CN 115616637A CN 202211612232 A CN202211612232 A CN 202211612232A CN 115616637 A CN115616637 A CN 115616637A
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grid
multipath
multipath error
model
training
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CN115616637B (en
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孙蕊
盛琪
魏华波
吉天翊
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Nanjing University of Aeronautics and Astronautics
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/13—Receivers
    • G01S19/22—Multipath-related issues
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/13—Receivers
    • G01S19/35—Constructional details or hardware or software details of the signal processing chain
    • G01S19/37—Hardware or software details of the signal processing chain

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Signal Processing (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)

Abstract

本发明提出了一种基于三维格网多径建模的城市复杂环境导航定位方法，包括：步骤1，区域三维格网模型构建，包括：构建GNSS训练数据集；构建区域三维格网布局；逐个格网层进行基于随机森林的多径误差建模；步骤2，调用区域三维格网模型，包括：GNSS测试数据集构建与遍历；根据各测试样本的先验位置匹配到相应格网层，调用多径误差模型并判定其可用性；得到修正后的定位结果。本发明充分考虑到不同高程上的反射环境，提升在崎岖路段、复杂立交环境以及无人机应用场景下的定位性能；将由NLOS和MI引起的多径误差作为一个整体进行建模和预测，故而不存在忽略某类NLOS或MI的问题。

Figure 202211612232

The present invention proposes a navigation and positioning method for urban complex environments based on three-dimensional grid multipath modeling, comprising: step 1, building a regional three-dimensional grid model, including: constructing a GNSS training data set; constructing a regional three-dimensional grid layout; The grid layer performs multipath error modeling based on random forest; step 2, calls the regional three-dimensional grid model, including: GNSS test data set construction and traversal; matches the corresponding grid layer according to the prior position of each test sample, and calls Multipath error model and determine its availability; get the corrected positioning results. The present invention fully considers the reflection environment on different elevations, and improves the positioning performance in rugged road sections, complex interchange environments, and UAV application scenarios; the multipath error caused by NLOS and MI is modeled and predicted as a whole, so There is no question of ignoring a certain class of NLOS or MI.

Figure 202211612232

Description

Urban complex environment navigation positioning method based on three-dimensional grid multipath modeling
Technical Field
The invention relates to a navigation and positioning method for an urban complex environment, in particular to a navigation and positioning method for an urban complex environment based on three-dimensional grid multipath modeling.
Background
In urban complex environments, GNSS (Global Navigation Satellite System) signals are easily blocked by tall buildings, reflected to generate MI (Multipath Interference) and NLOS (Non-line-of-Sight reception), and cannot be eliminated by difference. The GNSS data quality seriously affects the positioning result, and in the application scenario of daily life, production and navigation systems, the typical urban complex environment occupies a considerable proportion. Therefore, it is necessary to correct the multipath error of the GNSS observation data in a special area, such as a complex urban environment, so as to improve the performance thereof. At present, three solutions are provided for the multipath interference problem under the urban complex environment: methods for detecting interference components through signal processing, suppressing reflected signals through antenna design, and modeling based on observations.
The signal processing mainly comprises a correlator design method on a hardware level and a signal parameter estimation method based on combination of software and hardware, but direct and reflected signals must be received at the same time to be applied, and the situation only comprising NLOS cannot be processed. Antenna design methods include the use of choke antennas, right-hand polarized antennas, dual-polarized antennas, multi-antenna arrays, and the like. However, the method based on the antenna design is high in cost and large in antenna volume, is not suitable for positioning and navigation of a mobile carrier in a complex urban environment, and cannot eliminate high elevation angle reflected signals reflected by obstacles such as high buildings in the city.
The idea of the observation value modeling method is to perform signal classification by means of city 3D map assistance and machine learning mining of the relation between the observation value and the multipath error. A well-known shadow matching method uses a 3D city model to assist in simulating the visibility of each satellite at each candidate location, thereby matching the best candidate location. Using uncertainty models that take into account the urban building layout using 3D maps for urban canyon navigation can reduce the north maximum error from 13m to 2m. A simple and efficient 3D digital map is designed and manufactured in Jiang Dynasty, a satellite selection algorithm is designed on the basis of the map to eliminate obvious satellite signals which are not spread in sight distance, the accuracy of pseudo-range observation quantity is improved by improving the quality of observation data, but the Position Precision factor (PDOP) is reduced due to the reduction of the number of satellites, the number of available satellites in an urban environment is not enough, and the satellite lack phenomenon is more serious due to the satellite selection algorithm. Although the method using the city 3D map for assistance relieves the influence of NLOS, the judgment of the received signal extremely depends on the precision of the city 3D model, and the high-precision 3D model causes the time cost of calculation to be increased, cannot realize real-time positioning and cannot cope with the multipath effect caused by dynamic obstacles. The received satellite signals can be classified through machine learning, and the relation between the characteristics of GNSS original observed quantity in urban canyons and the satellite signal receiving type is learned through a Support Vector Machine (SVM), so that the former is utilized to predict the latter, and the accuracy can reach about 75%; the input features are selected as satellite elevation and signal intensity difference between two channels of a dual-polarized antenna, a decision tree is used for training a GNSS signal classifier, and the classification accuracy can reach 99%. The signal classification method based on machine learning usually eliminates the predicted NLOS signal, which causes the phenomenon of star deficiency existing in complex urban environments to be more serious.
In summary, the GNSS can provide Positioning, navigation, and Timing (PNT) information to the user, and the accuracy is very important. However, in an urban canyon environment, the blocking and reflection of GNSS signals by dense tall buildings cause severe Multipath Interference (MI) and non-line-of-sight reception (NLOS), and the GNSS signal quality is degraded, resulting in a great reduction in positioning accuracy. The prior art has the following defects:
a) The existing GNSS observation data plane modeling method cannot accurately correct users with different elevations. Due to the rugged road section and the complex interchange environment, different reflection environments are provided at different elevations of the same place. The existing navigation positioning method for performing regional plane grid division modeling on GNSS signals projects all users at the same place and different elevations to a two-dimensional plane, and performs multi-path error correction by using the same multi-path prediction model, so that pedestrians and vehicles at different elevations cannot be accurately positioned, and the method cannot cope with the accurate positioning condition in an unmanned aerial vehicle application scene.
b) The existing signal processing method can be used only by receiving direct and reflected signals at the same time, and cannot deal with the condition of only containing NLOS signals; the method based on antenna design can not eliminate high elevation NLOS/MI reflected by obstacles such as high buildings in cities.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to solve the technical problem of providing a navigation and positioning method for an urban complex environment based on three-dimensional grid multipath modeling aiming at the defects of the prior art.
In order to solve the technical problem, the invention discloses a navigation and positioning method for an urban complex environment based on three-dimensional grid multipath modeling, which comprises the following steps:
step 1, constructing a regional three-dimensional grid model, comprising:
step 1-1, constructing a GNSS training data set;
step 1-2, constructing a regional three-dimensional grid layout;
step 1-3, carrying out multipath error modeling based on random forests one by one in a grid layer;
step 2, calling the regional three-dimensional grid model, comprising:
step 2-1, construction and traversal of a GNSS test data set;
step 2-2, matching the prior position of each test sample to a corresponding grid layer, calling a multipath error model and judging the usability of the multipath error model;
and 2-3, obtaining a corrected positioning result, and completing the navigation positioning of the urban complex environment based on the three-dimensional grid multipath modeling.
Has the advantages that:
a) Aiming at the problem that the existing two-dimensional modeling auxiliary technology cannot provide accurate positioning for users with different elevations, the method further performs three-dimensional extension, selects height intervals to divide grid layers, models GNSS signals by taking the grid layers as units, fully considers reflection environments on different elevations, and improves the positioning performance on rugged road sections, complex interchange environments and unmanned aerial vehicle application scenes.
b) Aiming at the problems that the signal processing method can not deal with the situation only containing NLOS signals and the antenna design method can not deal with high elevation angle NLOS/MI, the invention models and predicts the multipath error caused by NLOS and MI as a whole, so the problem of neglecting some NLOS or MI does not exist.
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The foregoing and/or other advantages of the invention will become further apparent from the following detailed description of the invention when taken in conjunction with the accompanying drawings.
FIG. 1 is a schematic flow chart of the present invention.
Fig. 2 is a schematic diagram of a generation manner of a central point of a hexagonal grid.
Fig. 3 is a schematic plan projection view of a hexagonal grid column.
FIG. 4 is a schematic diagram of a regression model algorithm.
Detailed Description
The core content of the invention is a navigation positioning method for urban complex environment assisted by regional three-dimensional grid multipath modeling. The flow is shown in FIG. 1. The selected sensors are a GNSS receiver (capable of receiving beidou and GNSS signals) and an IMU (Inertial Measurement Unit, IMU). A city complex environment navigation positioning method based on three-dimensional grid multipath modeling comprises the following steps:
step 1, constructing a regional three-dimensional grid model, comprising:
step 1-1, constructing a GNSS training data set, wherein the specific method comprises the following steps:
repeatedly driving in the urban complex environment, collecting the original observed quantity output by the GNSS receiver, and extracting input features from the original observed quantity as samples; the input features include: pseudorange residuals
Figure 599392DEST_PATH_IMAGE001
Carrier to noise ratio
Figure 660758DEST_PATH_IMAGE002
Altitude angle of satellite
Figure 398032DEST_PATH_IMAGE003
And satellite azimuth
Figure 611976DEST_PATH_IMAGE004
The sample is:
Figure 766883DEST_PATH_IMAGE005
；
calibrating the sample with multipath error values, pseudorange error values
Figure 66277DEST_PATH_IMAGE006
The expression is as follows:
Figure 100002_DEST_PATH_IMAGE007
wherein c is the speed of light in vacuum,
Figure 669559DEST_PATH_IMAGE008
and
Figure 862643DEST_PATH_IMAGE009
for the satellite and receiver clock differences,
Figure 949635DEST_PATH_IMAGE010
and
Figure 736326DEST_PATH_IMAGE011
the uncorrected residuals in ionospheric and tropospheric delays respectively,
Figure 704151DEST_PATH_IMAGE012
errors due to multipath effects;
Figure 361528DEST_PATH_IMAGE012
the influence on pseudo range error accounts for a great proportion, so the above formula is regarded as multipath error;
after calibration is completed, resolving prior position of GNSS receiver
Figure 500648DEST_PATH_IMAGE013
And (3) bringing a training data set into correspondence with samples of corresponding epochs and multipath error values, and constructing to obtain the training data set, wherein the training samples are expressed as:
Figure 23902DEST_PATH_IMAGE014
。
step 1-2, constructing a regional three-dimensional grid layout, wherein the specific method comprises the following steps:
according to the geographic information of the city area provided by a geographic information system, matching the position data in the training data set to the corresponding area in the city map, and taking the minimum outsourcing square of the corresponding area
Figure 280571DEST_PATH_IMAGE015
A modeling area; one side of the square and the ENU coordinate system
Figure 74346DEST_PATH_IMAGE016
The axes are parallel and the side length is
Figure 492689DEST_PATH_IMAGE017
Rice; dividing a planar hexagonal grid on the modeling area; comprehensively setting the side length of the hexagonal grid according to the size of the modeling area and the positioning precision
Figure 237660DEST_PATH_IMAGE018
(ii) a Wherein, the lower limit value of the side length of the hexagonal grid
Figure 766861DEST_PATH_IMAGE019
The determination principle is as follows: ensuring that more than 80 percent of the total amount of training sample data in the grid is not less than 2000; upper limit of side length
Figure 290508DEST_PATH_IMAGE020
The determination principle is as follows: guarantee
Figure 660179DEST_PATH_IMAGE021
(ii) a If the upper limit value and the lower limit value of the side length are contradictory, the sampling rate is improved, and the method is important in urban complex environmentNewly acquiring GNSS data; starting from the end point along the north-south edges of the square
Figure 377599DEST_PATH_IMAGE022
Taking the interval of (A) as the center point of the hexagon and recording as
Figure 744381DEST_PATH_IMAGE023
, wherein
Figure 496437DEST_PATH_IMAGE024
For this number of points taken on the edge,
Figure 912374DEST_PATH_IMAGE025
(ii) a Then from
Figure 867823DEST_PATH_IMAGE023
Starting in the east-west direction
Figure 269986DEST_PATH_IMAGE026
The length of (c) takes the point, and all points are recorded as:
Figure 391394DEST_PATH_IMAGE027
, wherein
Figure 322441DEST_PATH_IMAGE028
The number of points on one east-west side of the square,
Figure 171711DEST_PATH_IMAGE029
；
to be provided with
Figure 95673DEST_PATH_IMAGE027
As the origin of coordinates, in the ENU coordinate system
Figure 822321DEST_PATH_IMAGE016
Shaft and
Figure 206160DEST_PATH_IMAGE030
the shaft is
Figure 651048DEST_PATH_IMAGE031
Shaft and
Figure 113122DEST_PATH_IMAGE032
axis, taking the origin of coordinates as the center of the hexagon and according to the selected side length of the hexagon
Figure 694276DEST_PATH_IMAGE018
Fix a vertex at
Figure 852944DEST_PATH_IMAGE032
Generating hexagons in each coordinate system on the axis in such a way, and carrying out dense arrangement on the whole modeling area;
densely arranging the above
Figure 768816DEST_PATH_IMAGE033
The two-dimensional grid is formed by extending in the height direction
Figure 50893DEST_PATH_IMAGE033
A grid column, wherein
Figure 502865DEST_PATH_IMAGE034
Height of each grid column
Figure 212195DEST_PATH_IMAGE035
The value of (a) is initially set to 100 meters, and is automatically updated in the subsequent steps according to the actual situation; to be provided with
Figure 349784DEST_PATH_IMAGE036
For spacing, each grid column is divided into
Figure 435552DEST_PATH_IMAGE037
The layer is used for modeling the GNSS signals by taking each three-dimensional grid layer as a unit;
Figure 476452DEST_PATH_IMAGE036
minimum value of (2)
Figure 356683DEST_PATH_IMAGE038
The determination principle is as follows: ensuring that more than 80% of the total amount of training sample data in the grid layer is not less than 500;
Figure 122514DEST_PATH_IMAGE036
maximum value of
Figure 231546DEST_PATH_IMAGE039
The determination principle is as follows: guarantee
Figure 641799DEST_PATH_IMAGE040
(ii) a If spacing
Figure 207778DEST_PATH_IMAGE036
If the maximum value and the minimum value of the GNSS data are contradictory, the sampling rate is increased, and the GNSS data are collected again in the urban environment.
1-3, carrying out random forest-based multipath error modeling one by one on grid layers, wherein the specific method comprises the following steps:
step 1-3-1, determining the grid layer to which each training sample belongs according to the three-dimensional grid layout constructed in step 1-2, wherein the specific method comprises the following steps:
the central point of the plane projection of the hexagonal grid column is
Figure DEST_PATH_IMAGE041
In the ENU coordinate system
Figure 18827DEST_PATH_IMAGE016
Shaft and
Figure 180819DEST_PATH_IMAGE030
the shafts respectively correspond to
Figure 694845DEST_PATH_IMAGE031
Shaft and
Figure 808557DEST_PATH_IMAGE032
axis, taking a training sample in the position
Figure 158767DEST_PATH_IMAGE042
And judging whether the hexagon belongs to the hexagon:
Figure 639296DEST_PATH_IMAGE043
only when both the above-mentioned expressions are satisfied, it is judged
Figure 24141DEST_PATH_IMAGE042
Within the grid posts; performing the determination on each training sample of the training data set to obtain a training data set of each grid column, and updating the height of each grid column
Figure 167808DEST_PATH_IMAGE044
, wherein
Figure DEST_PATH_IMAGE045
The maximum elevation value of the training sample in the training data set in the grid column is calculated, and the number of grid layers is updated simultaneously
Figure 192265DEST_PATH_IMAGE046
(ii) a And dividing the training samples into the grid layers according to the height data of each training sample in the training data set.
Step 1-3-2, performing multipath error model training based on random forests on each grid layer, wherein the specific method comprises the following steps:
dividing the set of training samples to which each grid layer belongs into
Figure 587737DEST_PATH_IMAGE030
Set of subsamples, preface
Figure 100002_DEST_PATH_IMAGE047
To obtain
Figure 545197DEST_PATH_IMAGE048
A sub-sample set, trained to obtain
Figure 843454DEST_PATH_IMAGE048
Calculating regression tree output valueMean and mean absolute error MAE; if MAE<0.05, then take the value
Figure 178709DEST_PATH_IMAGE030
Value, otherwise
Figure 486193DEST_PATH_IMAGE049
Repeating iteration; wherein the regression algorithm divides each sub-sample set into
Figure 704685DEST_PATH_IMAGE050
In the non-overlapping areas, a prediction result is obtained for each training sample in the area, and a division method for minimizing residual square sum RSS is found out, wherein the RSS calculation method comprises the following steps:
Figure 455735DEST_PATH_IMAGE051
wherein ,
Figure 267833DEST_PATH_IMAGE052
are divided into non-overlapping regions,
Figure 628276DEST_PATH_IMAGE053
j, the number of regions,
Figure 842220DEST_PATH_IMAGE054
is a first
Figure 498591DEST_PATH_IMAGE055
The label value of each of the training samples,
Figure 922619DEST_PATH_IMAGE056
indicating a predicted value of the jth region; the inner layer summation is to sum the squares of the difference values of the real values and the predicted values of all the training samples in the area, and the outer layer summation is to traverse all the divided areas; the process of minimizing RSS employs a recursive bisection method: when dividing the region, carrying out feature selection and node splitting according to the following formula until the region cannot be split:
Figure 837486DEST_PATH_IMAGE057
wherein ,
Figure 391089DEST_PATH_IMAGE058
the dimension representing the segmentation is the value of the input data,srepresenting a slicing point;
Figure 733209DEST_PATH_IMAGE059
and
Figure 503587DEST_PATH_IMAGE060
is shown in
Figure 956565DEST_PATH_IMAGE058
To cut the dimension, insTwo regions divided for the dividing points;
Figure 788778DEST_PATH_IMAGE030
the sub-sample set is branched in the above manner to obtain a regression tree model prediction rule, and the prediction result is represented by the following formula:
Figure 551067DEST_PATH_IMAGE061
in the formula ,
Figure 559474DEST_PATH_IMAGE062
for the prediction output of the regression tree model,
Figure 566875DEST_PATH_IMAGE063
in order to input the features of the image,
Figure 609918DEST_PATH_IMAGE064
；
Figure 277528DEST_PATH_IMAGE065
for the regression tree model prediction function, will
Figure 507652DEST_PATH_IMAGE030
The predicted outputs of the individual regression trees are averaged to obtain a predicted value of the multipath error as the output of the random forest
Figure 194111DEST_PATH_IMAGE066
；
Figure 340927DEST_PATH_IMAGE067
The multi-path error prediction rule is as follows:
Figure 195751DEST_PATH_IMAGE068
total number of grid layers divided by all grid columns
Figure 100002_DEST_PATH_IMAGE069
Representing, modeling the training data of each grid layer to obtain
Figure 522958DEST_PATH_IMAGE069
A multipath error model; the multipath error model accuracy is measured by the root mean square error RMSE:
Figure 855850DEST_PATH_IMAGE070
wherein ,
Figure 352779DEST_PATH_IMAGE071
is a first
Figure 378504DEST_PATH_IMAGE072
The root mean square error of each of the multipath error models,
Figure 98067DEST_PATH_IMAGE073
is a first
Figure 500229DEST_PATH_IMAGE055
Personal trainingThe true value of the multipath error of the training sample,
Figure 326365DEST_PATH_IMAGE074
is a first
Figure 788571DEST_PATH_IMAGE055
The predicted value of the multipath error model of each training sample,
Figure 870796DEST_PATH_IMAGE075
the number of training samples;
the precision parameters include: mean of root mean square errors of all grid models
Figure 561803DEST_PATH_IMAGE076
And standard deviation of
Figure 100002_DEST_PATH_IMAGE077
The first step
Figure 475401DEST_PATH_IMAGE072
Normalized processing result of root mean square error of individual grids
Figure 108508DEST_PATH_IMAGE078
Specific gravity of
Figure 772969DEST_PATH_IMAGE079
The calculation methods are listed as follows:
Figure 641568DEST_PATH_IMAGE080
Figure 488301DEST_PATH_IMAGE081
Figure 506024DEST_PATH_IMAGE082
Figure 438208DEST_PATH_IMAGE083
wherein ,
Figure 969552DEST_PATH_IMAGE084
is as follows
Figure 296891DEST_PATH_IMAGE072
The root mean square error of each of the multipath error models,
Figure 6221DEST_PATH_IMAGE085
the minimum of all multipath error models root mean square errors,
Figure 674968DEST_PATH_IMAGE086
the maximum of the root mean square error is modeled for all multipath errors.
1-3-3, completing the construction of a three-dimensional grid model of the region, comprising the following steps of:
and calculating to obtain the layout of the regional three-dimensional grid, the multipath error model of each grid layer, the multipath error prediction rule of the multipath error model and the precision parameters of each multipath error model.
Step 2, calling the regional three-dimensional grid model, comprising:
step 2-1, construction and traversal of a GNSS test data set, wherein the specific method comprises the following steps:
extracting a priori positions of test data from raw observations output by a user GNSS receiver
Figure 495157DEST_PATH_IMAGE013
The position precision factor PDOP, the pseudo-range residual error, the carrier-to-noise ratio, the satellite altitude and the satellite azimuth form a test sample to construct a test data set
Figure 801635DEST_PATH_IMAGE087
；
Traversing after the test data set is constructed, and calculating the following parameters: mean of the position accuracy factors PDOP of all test samples
Figure 72080DEST_PATH_IMAGE088
And standard deviation of
Figure 713277DEST_PATH_IMAGE089
First, a
Figure 87888DEST_PATH_IMAGE090
Normalized processing results of PDOP of each test sample
Figure 100002_DEST_PATH_IMAGE091
And specific gravity
Figure 950671DEST_PATH_IMAGE092
The calculation methods are listed below:
Figure 100002_DEST_PATH_IMAGE093
Figure 825305DEST_PATH_IMAGE094
Figure 100002_DEST_PATH_IMAGE095
Figure 140749DEST_PATH_IMAGE096
wherein ,
Figure 100002_DEST_PATH_IMAGE097
in order to test the number of data samples,
Figure 256735DEST_PATH_IMAGE098
is as follows
Figure 770762DEST_PATH_IMAGE090
The PDOP corresponding to each of the test samples,
Figure 992796DEST_PATH_IMAGE099
for the minimum of all the test samples PDOP,
Figure 234683DEST_PATH_IMAGE100
the maximum PDOP for all test samples.
Step 2-2, matching the prior position of each test sample to a corresponding grid layer, calling a multipath error model and judging the usability of the multipath error model, wherein the specific method comprises the following steps:
matching the test data to respective affiliated grid layers one by utilizing the existing three-dimensional grid layout, calling a multipath error model of the corresponding grid layer, and then judging the usability of the model based on the average value and the specific gravity according to the precision and the quality of the grid layer model; for data of a certain epoch, the following decisions are made:
Figure 449633DEST_PATH_IMAGE101
Figure 834478DEST_PATH_IMAGE102
wherein ,
Figure 243725DEST_PATH_IMAGE103
is a first
Figure 81231DEST_PATH_IMAGE090
The root mean square error value of the multipath error model corresponding to the grid to which the test data belongs,
Figure 975237DEST_PATH_IMAGE076
the mean of the root mean square errors of all the multipath error models,
Figure 982899DEST_PATH_IMAGE098
is as follows
Figure 546736DEST_PATH_IMAGE090
The individual test data corresponds to the PDOP of the epoch,
Figure 386385DEST_PATH_IMAGE088
the average value of all sample epochs in the test data set is PDOP; if a certain epoch data satisfies the two formulas, the corresponding multipath error model is judged to be available, the multipath error model is used for predicting the multipath error, and subsequent correction and final positioning calculation are carried out, otherwise, the judgment based on the proportion is carried out as follows:
Figure 693869DEST_PATH_IMAGE104
Figure 679405DEST_PATH_IMAGE105
wherein ,
Figure 928990DEST_PATH_IMAGE106
is as follows
Figure 475509DEST_PATH_IMAGE090
The specific gravity of the individual test data,
Figure 602996DEST_PATH_IMAGE107
if the epoch test sample meets the inequality, the multipath error model is judged to be available so as to carry out multipath prediction and correction, otherwise, the multipath error model is judged to be unavailable, and modeling correction is not carried out.
And 2-3, obtaining a corrected positioning result, and completing the navigation positioning of the urban complex environment based on the three-dimensional grid multipath modeling.
The obtaining of the corrected positioning result includes:
suppose that
Figure 816940DEST_PATH_IMAGE090
An observed pseudorange of one epoch of
Figure 112792DEST_PATH_IMAGE108
The multipath error value predicted by the multipath error model is
Figure 162918DEST_PATH_IMAGE109
And the corrected pseudo range is
Figure 812206DEST_PATH_IMAGE110
Using pseudorange location principles
Figure 129923DEST_PATH_IMAGE111
Instead of the former
Figure 472043DEST_PATH_IMAGE108
And calculating the corrected positioning solution.
Example (b):
as shown in FIG. 1, the present invention is divided into two parts, namely, the construction of the regional three-dimensional grid and the calling of the three-dimensional grid model. By utilizing the space-time repeatability of the satellite-city integral environment, a user can call the constructed three-dimensional grid model to predict multipath errors, so that the pseudo range is corrected. The method comprises the following specific steps:
1) Constructing a three-dimensional grid model of a region
Figure 878973DEST_PATH_IMAGE112
cWhich is the speed of light in a vacuum,
Figure 581218DEST_PATH_IMAGE113
and
Figure 504175DEST_PATH_IMAGE114
for the satellite and receiver clock-offsets,
Figure 767928DEST_PATH_IMAGE115
and
Figure 41915DEST_PATH_IMAGE116
uncorrectable residuals in ionospheric and tropospheric delays respectively,
Figure 423218DEST_PATH_IMAGE012
errors due to multipath effects.In the above-mentioned formula, the compound has the following structure,
Figure 216992DEST_PATH_IMAGE012
the influence on the pseudo range error accounts for a large proportion, so the above formula is regarded as the multipath error, and the sample is calibrated.
After calibration is completed, preprocessing is carried out on the GNSS initial training data set constructed in the steps, and the GNSS receiver resolves the prior position
Figure 635335DEST_PATH_IMAGE013
And (3) bringing the GNSS training data into a training set, and corresponding to samples of corresponding epochs and multipath errors one to construct a GNSS training data set, wherein the training samples are as follows:
Figure 380306DEST_PATH_IMAGE117
。
and secondly, constructing the three-dimensional grid layout of the region. The Geographic Information System (GIS) can provide Geographic Information of an urban area, match position data in the GNSS training data set to a corresponding area in the urban map, and select a minimum square capable of containing the positioning area
Figure 440666DEST_PATH_IMAGE015
A region is modeled. One side of the square and the ENU coordinate system
Figure 964314DEST_PATH_IMAGE016
The axes are parallel and the side length is
Figure 802826DEST_PATH_IMAGE017
And (4) rice. And performing planar hexagonal grid division on the square area. Comprehensively determining the side length of the hexagonal grid according to the size of the modeling area and the required positioning precision
Figure 51404DEST_PATH_IMAGE018
. In the practical application of the method, the air conditioner,
Figure 394749DEST_PATH_IMAGE018
the value of (c) needs to be set manually.Lower limit value of side length of hexagonal grid
Figure 146804DEST_PATH_IMAGE019
The determination principle is as follows: ensuring that more than 80 percent of the total amount of training sample data in the grid is not less than 2000; upper limit of side length
Figure 562742DEST_PATH_IMAGE020
The determination principle is as follows: guarantee
Figure 518191DEST_PATH_IMAGE021
. If the upper and lower limit values are contradictory, the sampling rate needs to be increased, and GNSS data is collected again for training in an urban environment. If set by person
Figure 654774DEST_PATH_IMAGE018
If the value does not meet the requirement, an error is prompted, and the setting needs to be carried out again. Selecting proper side length of hexagonal grid
Figure 41762DEST_PATH_IMAGE018
Due to the square shape
Figure 238388DEST_PATH_IMAGE015
And an edge of ENU coordinate system
Figure 556499DEST_PATH_IMAGE016
The axes are parallel, as shown in FIG. 2, starting from the end points along the north-south (side 1) of the square
Figure 11620DEST_PATH_IMAGE022
Taking the interval of (A) as the center point of the hexagon and recording as
Figure 738268DEST_PATH_IMAGE023
, wherein
Figure 856528DEST_PATH_IMAGE024
For this number of points taken on the edge,
Figure 426049DEST_PATH_IMAGE025
(ii) a Then from
Figure 904435DEST_PATH_IMAGE023
Starting in the direction of the east-west side ((2) side) of the square)
Figure 496041DEST_PATH_IMAGE026
The length of (d) is taken and all points are noted as:
Figure 34470DEST_PATH_IMAGE027
，
Figure 950342DEST_PATH_IMAGE029
wherein
Figure 124097DEST_PATH_IMAGE028
The number of points taken on the east-west side of a square. To be provided with
Figure 559758DEST_PATH_IMAGE027
As the origin of coordinates, the axes under the ENU coordinate system (east-north-sky coordinate system ENU, local Cartesian coordinates system) and
Figure 518355DEST_PATH_IMAGE030
the shaft is
Figure 937835DEST_PATH_IMAGE031
Shaft and
Figure 774335DEST_PATH_IMAGE032
axis, taking the origin of coordinates as the center of the hexagon and according to the selected side length of the hexagon
Figure 454715DEST_PATH_IMAGE018
Fix a vertex at
Figure 69368DEST_PATH_IMAGE032
On the shaft, as shown in fig. 3. Generating hexagons in each coordinate system in this wayAnd (4) carrying out dense arrangement on the whole modeling area.
Densely arranging the above
Figure 461297DEST_PATH_IMAGE069
The two-dimensional grid is formed by extending in the height direction
Figure 350755DEST_PATH_IMAGE069
A grid column, wherein
Figure 744697DEST_PATH_IMAGE118
And layering is performed. Height of each grid column
Figure 61408DEST_PATH_IMAGE119
Is initially set to 100 meters and is automatically updated in subsequent steps according to actual conditions so as to
Figure 833578DEST_PATH_IMAGE036
For spacing, each grid column is divided into
Figure 510416DEST_PATH_IMAGE037
And the layer is used for modeling the GNSS signals by taking each three-dimensional grid layer as a unit. In the practical application of the method, the air conditioner,
Figure 40754DEST_PATH_IMAGE036
the value of (b) needs to be set manually according to the actual situation and the required accuracy.
Figure 420045DEST_PATH_IMAGE036
Minimum value of (2)
Figure 285102DEST_PATH_IMAGE120
The determination principle is as follows: ensuring that more than 80% of the total amount of training sample data in the grid layer is not less than 500; maximum value of
Figure 516363DEST_PATH_IMAGE039
The determination principle is as follows: guarantee
Figure 386362DEST_PATH_IMAGE121
. If the upper and lower limit values are contradictory, the sampling rate needs to be increased, and GNSS data is collected again for training in an urban environment. If set manually
Figure 169510DEST_PATH_IMAGE122
If the value does not meet the requirement, an error is prompted, and the setting needs to be carried out again.
And thirdly, modeling the multipath error based on the random forest one by one grid layer.
And determining the lattice network layer of each training sample according to the three-dimensional lattice layout constructed in the second step.
As shown in FIG. 3, the central point of the planar projection of the hexagonal grid column is
Figure 272595DEST_PATH_IMAGE041
In the ENU coordinate system
Figure 527121DEST_PATH_IMAGE016
Shaft and
Figure 766473DEST_PATH_IMAGE030
the shafts respectively correspond to
Figure 579577DEST_PATH_IMAGE031
Shaft and
Figure 904379DEST_PATH_IMAGE032
axis, taking a training sample in the position
Figure 956736DEST_PATH_IMAGE042
And judging whether the hexagon belongs to the hexagon:
Figure 440807DEST_PATH_IMAGE123
determining only when both equations are satisfied
Figure 175545DEST_PATH_IMAGE042
Within the grid columns. For the training setPerforms the determination, updates the height of each grid column after obtaining a training data set for each grid column
Figure 738376DEST_PATH_IMAGE124
, wherein
Figure 849551DEST_PATH_IMAGE045
The elevation maximum value of the training data in the grid column is calculated, and the number of grid layers is updated simultaneously
Figure DEST_PATH_IMAGE125
. And dividing the training set into the grid layers according to the height data of each sample of the training set.
As shown in fig. 4, random forest based multipath error model training is performed on a trellis layer by trellis layer basis.
The first step in model training is to divide the sample set into
Figure 250446DEST_PATH_IMAGE030
Set of subsamples, preface
Figure 47763DEST_PATH_IMAGE047
To obtain
Figure 330845DEST_PATH_IMAGE048
A sub-sample set is obtained by training
Figure 245712DEST_PATH_IMAGE048
And (4) calculating a Mean value and an average Absolute Error (MAE) of the output values of the regression tree. If MAE<0.05, then take the value at that time
Figure 330473DEST_PATH_IMAGE030
Value, otherwise
Figure 672593DEST_PATH_IMAGE049
And repeating the iteration. The regression algorithm divides each subsample set into a plurality of non-overlapping regions, and a division method which enables residual square sum RSS to be minimum is found, wherein the RSS calculation method comprises the following steps:
Figure 177393DEST_PATH_IMAGE126
Figure DEST_PATH_IMAGE127
is divided into a plurality of non-overlapping areas,
Figure 719451DEST_PATH_IMAGE128
is the first region, the total number of regions is J,
Figure 157255DEST_PATH_IMAGE054
is as follows
Figure 670276DEST_PATH_IMAGE055
The label value of each of the training samples,
Figure 163836DEST_PATH_IMAGE056
indicating the prediction value of the jth region. The inner layer summation is to sum the squares of the difference values of the real values and the predicted values of all the training samples in the area, and the outer layer summation is to traverse all the divided areas. The process of minimizing RSS employs a recursive bisection method: when dividing the region, carrying out feature selection and node splitting according to the following formula until the splitting cannot be carried out:
Figure 545139DEST_PATH_IMAGE129
wherein ,
Figure 588181DEST_PATH_IMAGE130
representing the dimensions of the segmentation i.e. the values of the input data,srepresenting a cut point;
Figure 22836DEST_PATH_IMAGE059
and
Figure 518539DEST_PATH_IMAGE060
is shown in
Figure 562588DEST_PATH_IMAGE130
Is divided into cutting dimensions,sTwo regions divided for the dividing points;
Nthe sub-sample set is branched in the above manner to obtain a regression tree model prediction rule, and the prediction result can be expressed as:
Figure 351814DEST_PATH_IMAGE131
in the formula ,
Figure 941059DEST_PATH_IMAGE062
in order to predict the output for the model,
Figure 907747DEST_PATH_IMAGE063
in order to input the features of the image,
Figure 506218DEST_PATH_IMAGE064
；
Figure 268726DEST_PATH_IMAGE065
a function is predicted for the regression tree model. Each regression tree has a prediction output, will
Figure 294451DEST_PATH_IMAGE030
The predicted outputs of the individual regression trees are averaged to obtain a predicted value of the multipath error as the output of the random forest
Figure 748435DEST_PATH_IMAGE066
。
Figure 776696DEST_PATH_IMAGE067
The multipath error prediction rule can be expressed as follows for a multipath error model prediction function of random forest training:
Figure 383258DEST_PATH_IMAGE132
total number of grid layers
Figure 563572DEST_PATH_IMAGE069
Representing, modeling the data set of each grid layer to obtain
Figure 255585DEST_PATH_IMAGE069
A multipath error model. The multipath error model accuracy is measured by the root mean square error RMSE:
Figure 212170DEST_PATH_IMAGE133
wherein
Figure 938818DEST_PATH_IMAGE071
Is as follows
Figure 555613DEST_PATH_IMAGE050
The root-mean-square error of the individual models,
Figure 734922DEST_PATH_IMAGE073
is a first
Figure 104985DEST_PATH_IMAGE055
The true value of the multipath error for each training sample,
Figure 669828DEST_PATH_IMAGE074
is as follows
Figure 473836DEST_PATH_IMAGE055
The multi-path error model prediction value of each training sample,
Figure 643172DEST_PATH_IMAGE075
is the number of training samples. For subsequent grid availability determination, the following accuracy parameters are calculated: mean value of root mean square errors of multi-path error models of all grid layers
Figure 190828DEST_PATH_IMAGE134
Sum standard deviation
Figure 875756DEST_PATH_IMAGE077
First, a
Figure 585086DEST_PATH_IMAGE050
Normalized processing result of root mean square error of individual grids
Figure 100002_DEST_PATH_IMAGE135
Specific gravity of
Figure 552036DEST_PATH_IMAGE136
The calculation methods are listed as follows:
Figure 637804DEST_PATH_IMAGE137
Figure 944282DEST_PATH_IMAGE138
Figure 90093DEST_PATH_IMAGE139
Figure 855923DEST_PATH_IMAGE140
wherein ,
Figure 496114DEST_PATH_IMAGE084
is as follows
Figure 906367DEST_PATH_IMAGE072
The root mean square error of each of the multipath error models,
Figure 206767DEST_PATH_IMAGE085
the minimum of all multipath error models root mean square errors,
Figure 69681DEST_PATH_IMAGE086
the maximum of the root mean square error is modeled for all multipath errors.
And finishing the construction of the regional three-dimensional grid to obtain the layout of the regional three-dimensional grid, the multipath error prediction model of each grid layer, the multipath error prediction rule of the model and the precision parameters of each model.
2) Three-dimensional grid model invocation
Firstly, a GNSS test data set is constructed and traversed. Extracting a priori positions of test data from raw observations output by a user GNSS receiver
Figure 242125DEST_PATH_IMAGE013
Position Precision factor (PDOP), pseudo-range residual error, carrier-to-noise ratio, satellite altitude and satellite azimuth as test samples to construct GNSS test data set
Figure 506884DEST_PATH_IMAGE087
. Wherein the position is a priori
Figure 712606DEST_PATH_IMAGE013
The method is used for grid layer matching, PDOP is used for grid usability judgment, and other data are input into a model to predict multipath errors. For subsequent grid availability judgment, traversing is performed after the data set is constructed, and the following parameters are calculated: mean of all epochs PDOP
Figure 954494DEST_PATH_IMAGE088
And standard deviation of
Figure 920176DEST_PATH_IMAGE089
The first step
Figure 554288DEST_PATH_IMAGE090
Normalization processing result of single epoch PDOP
Figure 212803DEST_PATH_IMAGE091
Specific gravity of
Figure 535462DEST_PATH_IMAGE092
The calculation methods are listed as follows:
Figure 304835DEST_PATH_IMAGE141
Figure 934399DEST_PATH_IMAGE142
Figure 248968DEST_PATH_IMAGE143
Figure 308191DEST_PATH_IMAGE144
wherein
Figure 130522DEST_PATH_IMAGE097
In order to test the number of data samples,
Figure 489960DEST_PATH_IMAGE098
is as follows
Figure 235150DEST_PATH_IMAGE090
One test sample corresponds to the PDOP of an epoch,
Figure 781669DEST_PATH_IMAGE099
for the minimum of all the test samples PDOP,
Figure 17478DEST_PATH_IMAGE100
the maximum PDOP for all test samples.
And secondly, matching the prior positions of the test samples to corresponding grid layers, calling the models and judging the usability of the models. And matching the test data to respective affiliated grid layers one by utilizing the existing three-dimensional grid layout, calling a multipath error prediction model of the corresponding grid layer, and then judging the usability of the model based on the average value and the specific gravity according to the precision of the grid layer model and the quality of GNSS test data. For data of a certain epoch, the following decisions are made:
Figure DEST_PATH_IMAGE145
Figure 919837DEST_PATH_IMAGE146
Figure 340323DEST_PATH_IMAGE103
is a first
Figure 374138DEST_PATH_IMAGE090
The root mean square error value of the GNSS multi-path error model corresponding to the grid to which the test data belongs,
Figure 39737DEST_PATH_IMAGE134
is the average of the root mean square errors of all models,
Figure 232821DEST_PATH_IMAGE098
is as follows
Figure 309361DEST_PATH_IMAGE090
The individual test data corresponds to the PDOP of the epoch,
Figure 846784DEST_PATH_IMAGE088
is the average of all sample epops in the test data set. If a certain epoch data satisfies two formulas at the same time, the corresponding model is judged to be available, the multipath error can be predicted by the model, and subsequent correction and final positioning calculation are carried out, otherwise, the next judgment based on proportion is carried out:
Figure 565341DEST_PATH_IMAGE147
Figure 471986DEST_PATH_IMAGE148
wherein ,
Figure 985007DEST_PATH_IMAGE149
is as follows
Figure 738288DEST_PATH_IMAGE090
The specific gravity of the individual test data,
Figure 119591DEST_PATH_IMAGE150
is a preset specific gravity threshold value, and is based on the precision requirement required by positioning and the specific data quality pair
Figure 162633DEST_PATH_IMAGE150
Is appropriately adjusted. If the epoch test sample satisfies the inequality, the decision model is available for multi-path prediction and correction, otherwise, the decision is unavailable, and modeling correction is not performed.
And thirdly, obtaining a corrected positioning result. Suppose that
Figure 331708DEST_PATH_IMAGE090
An observed pseudorange of one epoch of
Figure 92991DEST_PATH_IMAGE108
The model predicted multipath error value is
Figure 137039DEST_PATH_IMAGE151
And the corrected pseudo range is
Figure 769009DEST_PATH_IMAGE110
Using pseudorange location principles
Figure 374565DEST_PATH_IMAGE111
Instead of the former
Figure 91985DEST_PATH_IMAGE108
And calculating the corrected positioning solution.
In specific implementation, the present application provides a computer storage medium and a corresponding data processing unit, where the computer storage medium is capable of storing a computer program, and the computer program, when executed by the data processing unit, may run the inventive content of the city complex environment navigation positioning method based on three-dimensional grid multi-path modeling and provided by the present invention and some or all steps in each embodiment. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), a Random Access Memory (RAM), or the like.
It is clear to those skilled in the art that the technical solutions in the embodiments of the present invention can be implemented by means of a computer program and its corresponding general-purpose hardware platform. Based on such understanding, the technical solutions in the embodiments of the present invention or portions thereof that contribute to the prior art may be embodied in the form of a computer program, that is, a software product, which may be stored in a storage medium and include several instructions for enabling a device (which may be a personal computer, a server, a single chip microcomputer, an MUU, or a network device) including a data processing unit to execute the method according to the embodiments or some portions of the embodiments of the present invention.
The invention provides a thought and a method of a city complex environment navigation positioning method based on three-dimensional grid multipath modeling, and a plurality of methods and ways for specifically implementing the technical scheme are provided. All the components not specified in the present embodiment can be realized by the prior art.

Claims (10)

1. A city complex environment navigation positioning method based on three-dimensional grid multipath modeling is characterized by comprising the following steps:
step 1, constructing a regional three-dimensional grid model, comprising:
step 1-1, constructing a GNSS training data set;
step 1-2, constructing a regional three-dimensional grid layout;
step 1-3, carrying out multipath error modeling based on random forests one by one in a grid layer;
step 2, calling the regional three-dimensional grid model, comprising:
step 2-1, constructing and traversing a GNSS test data set;
step 2-2, matching the prior positions of the test samples to corresponding grid layers, calling a multipath error model and judging the usability of the multipath error model;
and 2-3, obtaining a corrected positioning result, and completing the navigation positioning of the urban complex environment based on the three-dimensional grid multipath modeling.
2. The method for navigation and positioning in urban complex environment based on three-dimensional grid multipath modeling according to claim 1, wherein the method for constructing the GNSS training data set in step 1-1 specifically comprises:
repeatedly driving in the urban complex environment, collecting the original observed quantity output by the GNSS receiver, and extracting input features from the original observed quantity as samples; the input features include: pseudorange residuals
Figure DEST_PATH_IMAGE001
Carrier to noise ratio
Figure 328087DEST_PATH_IMAGE002
Altitude angle of satellite
Figure 165593DEST_PATH_IMAGE004
And satellite azimuth
Figure DEST_PATH_IMAGE005
The sample is:
Figure DEST_PATH_IMAGE007
；
calibrating the sample with multipath error values, pseudorange error values
Figure 187163DEST_PATH_IMAGE008
The expression is as follows:
Figure 472520DEST_PATH_IMAGE010
wherein c is the speed of light in vacuum,
Figure DEST_PATH_IMAGE011
and
Figure 364252DEST_PATH_IMAGE012
for the satellite and receiver clock differences,
Figure DEST_PATH_IMAGE013
and
Figure 404234DEST_PATH_IMAGE014
the uncorrected residuals in ionospheric and tropospheric delays respectively,
Figure DEST_PATH_IMAGE015
errors due to multipath effects;
after calibration is completed, the prior position solved by the GNSS receiver is determined
Figure 554461DEST_PATH_IMAGE016
And (3) bringing a training data set into correspondence with samples of corresponding epochs and multipath error values, and constructing to obtain the training data set, wherein the training samples are expressed as:
Figure 913898DEST_PATH_IMAGE018
。
3. the method for navigating and positioning the urban complex environment based on the three-dimensional grid multipath modeling according to claim 2, wherein the step 1-2 of constructing the three-dimensional grid layout of the area comprises the following specific steps:
according to the geographic information of the urban area provided by a geographic information system, the position number in the training data set is countedAccording to the corresponding area matched into the city map, taking the minimum outsourcing square of the corresponding area
Figure DEST_PATH_IMAGE019
A modeling area; one side of the square and the ENU coordinate system
Figure 570008DEST_PATH_IMAGE020
The axes are parallel and the side length is
Figure DEST_PATH_IMAGE021
Rice; dividing a planar hexagonal grid on the modeling area; comprehensively setting the side length of the hexagonal grid according to the size of the modeling area and the positioning precision
Figure 962199DEST_PATH_IMAGE022
(ii) a Wherein, the lower limit value of the side length of the hexagonal grid
Figure DEST_PATH_IMAGE023
The determination principle is as follows: ensuring that more than 80 percent of the total amount of training sample data in the grid is not less than 2000; upper limit of side length
Figure 57063DEST_PATH_IMAGE024
The determination principle is as follows: guarantee
Figure DEST_PATH_IMAGE025
(ii) a If the upper limit value and the lower limit value of the side length of the hexagonal grid are contradictory, the sampling rate is improved, and GNSS data are collected again in the urban complex environment; starting from the end point along the north-south edges of the square
Figure 926799DEST_PATH_IMAGE026
Taking the interval of (A) as the center point of the hexagon and recording as
Figure DEST_PATH_IMAGE027
, wherein
Figure 688563DEST_PATH_IMAGE028
For this number of points taken on the edge,
Figure DEST_PATH_IMAGE029
(ii) a Then from
Figure 643750DEST_PATH_IMAGE027
Starting in the east-west direction
Figure 542304DEST_PATH_IMAGE030
The length of (d) is taken and all points are noted as:
Figure DEST_PATH_IMAGE031
, wherein
Figure 63284DEST_PATH_IMAGE032
The number of points on one east-west side of the square,
Figure DEST_PATH_IMAGE033
；
to be provided with
Figure 1809DEST_PATH_IMAGE031
As the origin of coordinates, in the ENU coordinate system
Figure 506609DEST_PATH_IMAGE020
Shaft and
Figure 490745DEST_PATH_IMAGE034
the shaft is
Figure DEST_PATH_IMAGE035
Shaft and
Figure 335073DEST_PATH_IMAGE036
axis, taking the origin of coordinates as the center of the hexagon and according to the selected side length of the hexagon
Figure 503886DEST_PATH_IMAGE022
Fix a vertex at
Figure 493052DEST_PATH_IMAGE036
Generating hexagons in each coordinate system on the axis in such a way, and performing dense arrangement on the whole modeling area;
densely arranging the above
Figure DEST_PATH_IMAGE037
The two-dimensional grid is formed by extending in the height direction
Figure 467830DEST_PATH_IMAGE037
A grid column, wherein
Figure 979714DEST_PATH_IMAGE038
Height of each grid column
Figure DEST_PATH_IMAGE039
The value of (a) is initially set to 100 meters, and is automatically updated in the subsequent steps according to the actual situation; to be provided with
Figure 240800DEST_PATH_IMAGE040
For spacing, each grid column is divided into
Figure 267662DEST_PATH_IMAGE042
A layer;
Figure 49061DEST_PATH_IMAGE040
minimum value of (2)
Figure DEST_PATH_IMAGE043
The determination principle is as follows: ensuring that the total amount of training sample data in more than 80 percent of grid layers is not less than 500;
Figure 274506DEST_PATH_IMAGE040
maximum value of
Figure 113018DEST_PATH_IMAGE044
The determination principle is as follows: guarantee
Figure 892755DEST_PATH_IMAGE046
(ii) a If spacing
Figure 474915DEST_PATH_IMAGE040
If the maximum value and the minimum value of the GNSS data are contradictory, the sampling rate is increased, and the GNSS data are collected again in the urban environment.
4. The method for navigating and positioning the urban complex environment based on the three-dimensional grid multipath modeling as claimed in claim 3, wherein the random forest-based multipath error modeling is performed on grid layers one by one in steps 1-3, and the specific method comprises:
step 1-3-1, determining a grid layer to which each training sample belongs according to the three-dimensional grid layout constructed in the step 1-2;
step 1-3-2, performing random forest-based multipath error model training on each grid layer;
and 1-3-3, completing construction of the regional three-dimensional grid model.
5. The method for navigating and positioning the urban complex environment based on the three-dimensional grid multipath modeling according to claim 4, wherein the step 1-3-1 of determining the grid layer to which each training sample belongs comprises:
the central point of the plane projection of the hexagonal grid column is
Figure DEST_PATH_IMAGE047
In the ENU coordinate system
Figure 903270DEST_PATH_IMAGE020
Shaft and
Figure 850366DEST_PATH_IMAGE034
the shafts respectively correspond to
Figure 789504DEST_PATH_IMAGE035
Shaft and
Figure 972092DEST_PATH_IMAGE036
axis, taking a training sample in the position
Figure 47496DEST_PATH_IMAGE048
And judging whether the hexagon belongs to the hexagon:
Figure 165493DEST_PATH_IMAGE050
only when both of the above-described equations are satisfied, it is determined
Figure 109703DEST_PATH_IMAGE048
Within the grid column; executing the judgment on each training sample of the training data set to obtain the training data set of each grid column, and updating the height of each grid column
Figure 518819DEST_PATH_IMAGE052
, wherein
Figure DEST_PATH_IMAGE053
The maximum elevation value of the sample in the training data set in the grid column is calculated, and the number of grid layers is updated simultaneously
Figure 697996DEST_PATH_IMAGE042
(ii) a And dividing the training samples into the grid layers according to the height data of each sample in the training data set.
6. The method for navigating and positioning the urban complex environment based on the three-dimensional grid multipath modeling as claimed in claim 5, wherein the step 1-3-2 of training the multipath error model based on the random forest for each grid layer comprises:
dividing the set of training samples to which each grid layer belongs into
Figure 314791DEST_PATH_IMAGE034
Set of subsamples, preface
Figure 149892DEST_PATH_IMAGE054
To obtain
Figure DEST_PATH_IMAGE055
A sub-sample set, trained to obtain
Figure 546720DEST_PATH_IMAGE055
Calculating a mean value and an average absolute error MAE of the output values of the regression tree; if MAE<0.05, then take the value
Figure 127874DEST_PATH_IMAGE034
Value, otherwise
Figure 181149DEST_PATH_IMAGE056
Repeating iteration; the regression algorithm divides each subsample set into j non-overlapping regions, then calculates a prediction result for each training sample in the region, and finds a division method which minimizes residual square sum RSS, and the RSS calculation method is as follows:
Figure 847754DEST_PATH_IMAGE058
wherein ,
Figure 785623DEST_PATH_IMAGE060
are divided into non-overlapping regions,
Figure 204972DEST_PATH_IMAGE062
is the jth zoneThe number of domains, regions is J,
Figure DEST_PATH_IMAGE063
is as follows
Figure 369761DEST_PATH_IMAGE064
The label value of each of the training samples,
Figure DEST_PATH_IMAGE065
indicating a predicted value of the jth region; the inner layer summation is to sum the squares of the difference values of the real values and the predicted values of all the training samples in the area, and the outer layer summation is to traverse all the divided areas; the process of minimizing RSS employs a recursive bisection method: when dividing the region, carrying out feature selection and node splitting according to the following formula until the splitting cannot be carried out:
Figure DEST_PATH_IMAGE067
wherein ,
Figure DEST_PATH_IMAGE068A
representing the dimensions of the segmentation i.e. the values of the input data,srepresenting a cut point;
Figure DEST_PATH_IMAGE069
and
Figure 897564DEST_PATH_IMAGE070
is shown in
Figure DEST_PATH_IMAGE068AA
To cut the dimension, insTwo regions divided for the dividing points;
Figure 760828DEST_PATH_IMAGE034
the sub-sample set is branched in the way to obtain a regression tree model prediction rule and a prediction resultThe fruit is represented by the formula:
Figure 300262DEST_PATH_IMAGE072
in the formula ,
Figure DEST_PATH_IMAGE073
is the output of the regression tree model prediction,
Figure 898603DEST_PATH_IMAGE074
in order to input the features of the image,
Figure 8641DEST_PATH_IMAGE076
；
Figure DEST_PATH_IMAGE077
for the regression tree model prediction function, will
Figure 478193DEST_PATH_IMAGE034
The predicted outputs of the individual regression trees are averaged to obtain a predicted value of the multipath error as the output of the random forest
Figure 622867DEST_PATH_IMAGE078
；
Figure DEST_PATH_IMAGE079
The multipath error prediction rule is that the multipath error prediction function of the multipath error model trained by the random forest is as follows:
Figure DEST_PATH_IMAGE081
total number of grid layers divided by all grid columns
Figure 313480DEST_PATH_IMAGE082
Representing, modeling the training data of each grid layer to obtain
Figure 94835DEST_PATH_IMAGE082
A multipath error model; the multipath error model accuracy is measured by the root mean square error RMSE:
Figure 991247DEST_PATH_IMAGE084
wherein ,
Figure DEST_PATH_IMAGE085
is as follows
Figure 239695DEST_PATH_IMAGE086
The root mean square error of each of the multipath error models,
Figure DEST_PATH_IMAGE087
is as follows
Figure 304472DEST_PATH_IMAGE064
The true value of the multipath error for each training sample,
Figure 654682DEST_PATH_IMAGE088
is as follows
Figure 606982DEST_PATH_IMAGE064
The multi-path error model prediction value of each training sample,
Figure DEST_PATH_IMAGE089
the number of training samples;
the precision parameters include: mean of root mean square errors of all grid models
Figure 647619DEST_PATH_IMAGE090
Sum standard deviation
Figure DEST_PATH_IMAGE091
The first step
Figure 758663DEST_PATH_IMAGE086
Normalized processing result of root mean square error of individual grids
Figure 517541DEST_PATH_IMAGE092
Specific gravity of
Figure DEST_PATH_IMAGE093
The calculation methods are listed below:
Figure DEST_PATH_IMAGE095
Figure DEST_PATH_IMAGE097
Figure DEST_PATH_IMAGE099
Figure DEST_PATH_IMAGE101
wherein ,
Figure 415743DEST_PATH_IMAGE102
is as follows
Figure 373204DEST_PATH_IMAGE086
The root mean square error of each of the multipath error models,
Figure DEST_PATH_IMAGE103
the minimum of all multipath error models root mean square errors,
Figure 658079DEST_PATH_IMAGE104
the maximum of the root mean square error is modeled for all multipath errors.
7. The method for navigating and positioning the urban complex environment based on the three-dimensional grid multipath modeling according to claim 6, wherein the step 1-3-3 of completing the regional three-dimensional grid model construction comprises the following steps:
and calculating to obtain the three-dimensional grid layout of the region, the multipath error model of each grid layer, the multipath error prediction rule of the multipath error model and the precision parameters of each multipath error model.
8. The method for navigation and positioning in urban complex environment based on three-dimensional grid multipath modeling according to claim 7, wherein the GNSS test data set in step 2-1 is constructed and traversed, and the specific method comprises:
extracting a priori positions of test data from raw observations output by a user GNSS receiver
Figure 982881DEST_PATH_IMAGE016
The position precision factor PDOP, the pseudo-range residual error, the carrier-to-noise ratio, the satellite altitude and the satellite azimuth form a test sample to construct a GNSS test data set
Figure 680579DEST_PATH_IMAGE106
；
Traversing after the construction of the GNSS test data set is completed, and calculating the following parameters: mean of the position accuracy factors PDOP of all test samples
Figure DEST_PATH_IMAGE107
Sum standard deviation
Figure 758125DEST_PATH_IMAGE108
The first step
Figure DEST_PATH_IMAGE109
Normalized processing result of PDOP of each test sample
Figure 414235DEST_PATH_IMAGE110
Specific gravity of
Figure 879195DEST_PATH_IMAGE112
The calculation methods are listed as follows:
Figure 239638DEST_PATH_IMAGE114
Figure 719161DEST_PATH_IMAGE116
Figure 874068DEST_PATH_IMAGE118
Figure 642304DEST_PATH_IMAGE120
wherein ,
Figure DEST_PATH_IMAGE121
in order to test the number of samples,
Figure 871684DEST_PATH_IMAGE122
is as follows
Figure 736872DEST_PATH_IMAGE109
The PDOP corresponding to each of the test samples,
Figure DEST_PATH_IMAGE123
for the minimum of all the test samples PDOP,
Figure 734784DEST_PATH_IMAGE124
for all test samples PDThe maximum value of OP.
9. The method for navigating and positioning the urban complex environment based on the three-dimensional grid multipath modeling according to claim 8, wherein the step 2-2 of matching the prior position of each test sample to the corresponding grid layer, calling the model and determining the usability thereof comprises:
matching the test data to respective affiliated grid layers one by utilizing the existing three-dimensional grid layout, calling a multipath error model of a corresponding grid layer, and then judging the usability of the model based on an average value and a specific gravity according to the precision of the grid layer model and the quality of the test data; for data of a certain epoch, the following judgment is made:
Figure 177267DEST_PATH_IMAGE126
Figure 145092DEST_PATH_IMAGE128
wherein ,
Figure DEST_PATH_IMAGE129
is as follows
Figure 986490DEST_PATH_IMAGE109
The root mean square error value of the multipath error model corresponding to the grid to which the test data belongs,
Figure 155303DEST_PATH_IMAGE090
the mean of the root mean square errors of all the multipath error models,
Figure 163710DEST_PATH_IMAGE122
is a first
Figure 138489DEST_PATH_IMAGE109
The individual test data corresponds to the PDOP of the epoch,
Figure 447110DEST_PATH_IMAGE107
the average value of all sample epochs in the test data set is PDOP; if a certain epoch data simultaneously satisfies the two formulas, the corresponding multipath error model is judged to be available, the multipath error is predicted by the multipath error model, and subsequent correction and final positioning calculation are carried out, otherwise, the judgment based on the proportion is carried out as follows:
Figure DEST_PATH_IMAGE131
Figure DEST_PATH_IMAGE133
wherein ,
Figure 914388DEST_PATH_IMAGE134
is as follows
Figure 128201DEST_PATH_IMAGE109
The specific gravity of the individual test data,
Figure DEST_PATH_IMAGE135
if the epoch test sample meets the inequality, judging that the multipath error model is available to predict and correct the multipath error, and otherwise, judging that the multipath error model is unavailable and not carrying out modeling correction.
10. The method for navigating and positioning an urban complex environment based on three-dimensional grid multipath modeling according to claim 9, wherein the obtaining of the corrected positioning result in the step 2-3 comprises:
suppose that
Figure 313194DEST_PATH_IMAGE109
An observed pseudorange of one epoch of
Figure 194431DEST_PATH_IMAGE136
The multipath error value predicted by the multipath error model is
Figure DEST_PATH_IMAGE137
And the corrected pseudo range is
Figure 498855DEST_PATH_IMAGE138
Using the principle of pseudo-range positioning
Figure DEST_PATH_IMAGE139
Instead of the former
Figure 403226DEST_PATH_IMAGE136
And calculating the corrected positioning solution.
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