CN108761435B - Fingerprint optimization method based on normal distribution signal - Google Patents
Fingerprint optimization method based on normal distribution signal Download PDFInfo
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- CN108761435B CN108761435B CN201810380949.7A CN201810380949A CN108761435B CN 108761435 B CN108761435 B CN 108761435B CN 201810380949 A CN201810380949 A CN 201810380949A CN 108761435 B CN108761435 B CN 108761435B
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- 238000005457 optimization Methods 0.000 title claims abstract description 17
- 230000008030 elimination Effects 0.000 claims description 4
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- 238000005070 sampling Methods 0.000 claims description 4
- 238000004891 communication Methods 0.000 description 2
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- 230000009286 beneficial effect Effects 0.000 description 1
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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
- G01S11/00—Systems for determining distance or velocity not using reflection or reradiation
- G01S11/02—Systems for determining distance or velocity not using reflection or reradiation using radio waves
- G01S11/06—Systems for determining distance or velocity not using reflection or reradiation using radio waves using intensity measurements
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
- G01C21/206—Instruments for performing navigational calculations specially adapted for indoor navigation
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/33—Services specially adapted for particular environments, situations or purposes for indoor environments, e.g. buildings
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
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Abstract
本发明公开了一种基于正态分布信号的指纹优化方法，包括以下步骤：1)采集离线指纹；2)对步骤1)采集得到的离线指纹进行优化；3)采集在线指纹；4)通过步骤2)优化后的离线指纹对步骤3)采集得到的在线指纹进行优化，完成基于正态分布信号的指纹优化，该方法能够实现室内定位终端的精确定位。
The invention discloses a fingerprint optimization method based on a normal distribution signal, comprising the following steps: 1) collecting offline fingerprints; 2) optimizing the offline fingerprints obtained in step 1); 3) collecting online fingerprints; 4) passing the steps 2) The optimized offline fingerprint The online fingerprint obtained in step 3) is optimized, and the fingerprint optimization based on the normal distribution signal is completed, and the method can realize the precise positioning of the indoor positioning terminal.
Description
Technical Field
The invention belongs to the field of wireless communication, pattern recognition and indoor positioning, and relates to a fingerprint optimization method based on a normal distribution signal.
Background
The technology of Wireless communication, computers and intelligent terminals is becoming mature, so that the fingerprint indoor positioning system based on Wireless Local Area Network (WLAN) is rapidly developed. The fingerprint method positioning has the advantages that the user can realize positioning only by using the intelligent terminal in the WLAN environment, and the limitation of additional hardware deployment is eliminated. Indoor positioning based on WLAN fingerprint method is mainly divided into off-line acquisition stage and on-line positioning stage. In the off-line acquisition stage, a terminal acquisition device is used for acquiring the signal intensity value of each Access Point (AP) in the environment on a pre-selected off-line reference Point. The position coordinates of each offline reference point, a corresponding Received Signal Strength (RSS) value and a Media Access Control (MAC) address of the AP are combined to form an offline fingerprint, and a set of all the offline fingerprints forms an offline fingerprint database. In the on-line positioning stage, the RSS values of the APs in the environment are collected by the terminal collecting equipment, the RSS values and the corresponding MAC addresses form on-line fingerprints, and the on-line fingerprints are matched with the off-line fingerprints in the off-line fingerprint database, so that the terminal position information is obtained. In WLAN-based indoor positioning by a fingerprint method, fingerprints used for positioning are vectors consisting of AP signal strength components, and in the prior art, unprocessed original offline fingerprints and unprocessed online fingerprints are generally directly used for matching and positioning. However, due to the influence of indoor obstacle blocking, same-frequency radio interference and the like, part of the AP signal intensity components in the fingerprint are interfered and correct information cannot be reflected, so that the positioning accuracy of the indoor positioning terminal is low.
Disclosure of Invention
The present invention is directed to overcome the above disadvantages of the prior art, and provides a fingerprint optimization method based on normal distribution signals, which can achieve accurate positioning of an indoor positioning terminal.
In order to achieve the above object, the fingerprint optimization method based on normal distribution signals according to the present invention comprises the following steps:
1) collecting an off-line fingerprint;
2) optimizing the offline fingerprint acquired in the step 1);
3) collecting online fingerprints;
4) optimizing the online fingerprint acquired in the step 3) through the optimized offline fingerprint in the step 2), and finishing fingerprint optimization based on the normal distribution signal.
The specific operation of the step 1) is as follows: and setting a fixed sampling time length on each off-line reference point, and then continuously acquiring the received signal strength samples of each AP in the environment at a fixed frequency.
The specific operation of the step 2) is as follows:
a1) setting the received signal strength sample collected by each AP on the off-line reference point as rssi1,rssi2,...,rssinRespectively calculating the average value of the received signal strength samples collected by each AP
And variance
Wherein n is the number of samples, and then a threshold omega is set1Then eliminate the variance σ2>Ω1A corresponding AP;
a2) constructing a mean sequence by the mean values corresponding to the residual APs after the elimination in the step a1)
Obtaining the mean value of the mean value sequence
And variance
Wherein N is the number of off-line reference points with corresponding AP, and then a threshold omega is set2Then eliminate the variance
The corresponding AP.
The specific operation of the step 3) is as follows: and collecting received signal strength samples of all APs in the environment at any moment on all online test points to form single online fingerprint samples.
The specific operation of the step 4) is as follows:
b1) dividing the test area into a plurality of sub-areas with equal areas and no overlap, wherein each sub-area contains the same number of off-line reference points, and positioning by utilizing the on-line fingerprints to judge the sub-area where the positioning terminal is located;
b2) for any AP, a plurality of groups of received signal strength samples collected by the AP in a period of time on an off-line reference point j are set as
The average of the multiple sets of received signal strength samples
Standard deviation of the multiple groups of received signal strength samples
j ═ 1.. K, where n is the number of samples and K is the number of offline reference points where the AP appearsWherein the measured value of the AP at the online stage is rssi';
order to
Counting whether | v 'is satisfied in each offline reference point in the sub-region determined in the step b 1)'j|<3σjThe number M of the off-line reference points is set, then a threshold omega is set, and finally the AP corresponding to the threshold omega with the M smaller than the threshold omega is removed.
The invention has the following beneficial effects:
the fingerprint optimization method based on the normal distribution signal firstly collects the off-line fingerprints and then optimizes the off-line fingerprints to remove the off-line fingerprints with larger errors; and then, acquiring online fingerprints, and finally optimizing the online fingerprints through the optimized offline fingerprints to remove the online fingerprints with larger errors, so that the indoor positioning accuracy of the positioning terminal is improved.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a plan view of a test environment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the accompanying drawings:
referring to fig. 1, the fingerprint optimization method based on normal distribution signals according to the present invention includes the following steps:
1) collecting an off-line fingerprint;
the specific operation of the step 1) is as follows: and setting a fixed sampling time length on each off-line reference point, and then continuously acquiring the received signal strength samples of each AP in the environment at a fixed frequency.
2) Optimizing the offline fingerprint acquired in the step 1);
the specific operation of the step 2) is as follows:
a1) setting the received signal strength sample collected by each AP on the off-line reference point as rssi1,rssi2,...,rssinRespectively calculating the average value of the received signal strength samples collected by each AP
And variance
Wherein n is the number of samples, and then a threshold omega is set1Then eliminate the variance σ2>Ω1A corresponding AP;
a2) constructing a mean sequence by the mean values corresponding to the residual APs after the elimination in the step a1)
Obtaining the mean value of the mean value sequence
And variance
Wherein N is the number of off-line reference points with corresponding AP, and then a threshold omega is set2Then eliminate the variance
The corresponding AP.
3) Collecting online fingerprints;
the specific operation of the step 3) is as follows: and collecting received signal strength samples of all APs in the environment at any moment on all online test points to form single online fingerprint samples.
4) Optimizing the online fingerprint acquired in the step 3) through the optimized offline fingerprint in the step 2), and finishing fingerprint optimization based on the normal distribution signal.
The specific operation of the step 4) is as follows:
b1) dividing the test area into a plurality of sub-areas with equal areas and no overlap, wherein each sub-area contains the same number of off-line reference points, and positioning by utilizing the on-line fingerprints to judge the sub-area where the positioning terminal is located;
b2) for any AP, a plurality of groups of received signal strength samples collected by the AP in a period of time on an off-line reference point j are set as
The average of the multiple sets of received signal strength samples
Standard deviation of the multiple groups of received signal strength samples
j ═ 1.. K, where n is the number of samples and K is the number of off-line reference points where the AP appears, where the measured value of the AP at the on-line stage is rssi';
order to
Counting whether | v 'is satisfied in each offline reference point in the sub-region determined in the step b 1)'j|<3σjThe number M of the off-line reference points is set, then a threshold omega is set, and finally the AP corresponding to the threshold omega with the M smaller than the threshold omega is removed.
Example one
The test environment of this embodiment is a typical office area, the size of the whole test environment is 52.0m × 60.0m, the specific test environment is shown in fig. 2, the terminal acquisition device is a glory Magic smartphone, and the specific operation process is as follows:
1) collecting an off-line fingerprint; uniformly selecting offline reference points in an experimental environment at intervals of 3m, wherein the whole experimental environment comprises 135 offline reference points, and continuously acquiring AP received signal strength samples of 60s at sampling intervals of 200ms on each offline reference point;
2) after the off-line fingerprint collection in the step 1) is finished, a plurality of groups of received signal strength samples rssi collected by any AP on an off-line reference point1,rssi2,...,rssinRespectively calculate the mean value thereof
And variance
Wherein n is a sampleCounting, then setting threshold omega115, then σ will be satisfied2>Ω1Removing the AP;
3) constructing a mean value sequence by the corresponding mean values of the residual APs after the elimination in the step 2)
Calculating the corresponding mean value of the mean value sequence
And variance
Wherein, N is the number of the off-line reference points with the corresponding AP, and a threshold omega is set210, then will satisfy
Removing the AP;
4) collecting online fingerprints; randomly selecting online test points in an experimental environment, and scanning each online test point once, namely only collecting a group of AP received signal strength values as online fingerprints;
5) uniformly dividing the test environment into four sub-regions, wherein the area of each sub-region is about 25.0m multiplied by 13.0m, and each sub-region comprises about 35 off-line reference points; carrying out coarse positioning by using the original online fingerprint, and judging a subregion where the positioning terminal is located;
6) for any AP, a plurality of groups of received signal strength samples collected in a period of time on an off-line reference point j are set as
Then the mean value is
Standard deviation of
Wherein n is the number of samples, K is the number of off-line reference points where the AP appears, and the measured value of the AP at the on-line stage is rssi';
order to
Counting whether | v 'is satisfied in each off-line reference point in the sub-region judged in the step 5)'j|<3σjThe number M of the off-line reference points is set to 1, and then the off-line reference points are eliminated to meet the requirement of M<AP corresponding to omega;
the positioning result of this embodiment is shown in table 1, and the comparison method is a WLAN-based fingerprint indoor positioning method without fingerprint optimization; as can be seen from Table 1, the average positioning error and the 95% positioning error of the invention are both obviously reduced, which proves that the invention can effectively improve the average precision and robustness of the positioning system.
TABLE 1
Claims (4)
1. A fingerprint optimization method based on normal distribution signals is characterized by comprising the following steps:
1) collecting an off-line fingerprint;
2) optimizing the offline fingerprint acquired in the step 1);
3) collecting online fingerprints;
4) optimizing the online fingerprint acquired in the step 3) through the optimized offline fingerprint in the step 2), and finishing fingerprint optimization based on a normal distribution signal;
the specific operation of the step 4) is as follows:
b1) dividing the test area into a plurality of sub-areas with equal areas and no overlap, wherein each sub-area contains the same number of off-line reference points, and positioning by utilizing the on-line fingerprints to judge the sub-area where the positioning terminal is located;
b2) for any one AP, wherein the AP is a wireless access point, a plurality of groups of received signal strength samples collected by the AP in a period of time on an off-line reference point j are set as
The average of the multiple sets of received signal strength samples
Standard deviation of the multiple groups of received signal strength samples
Wherein n is the number of samples, and K is the number of off-line reference points where the AP appears, wherein the measured value of the AP at the on-line stage is rsi';
order to
Counting whether | v 'is satisfied in each offline reference point in the sub-region determined in the step b 1)'j|<3σjThe number M of the off-line reference points is set, then a threshold omega is set, and finally the AP corresponding to the threshold omega with the M smaller than the threshold omega is removed.
2. The normal distribution signal-based fingerprint optimization method according to claim 1, wherein the specific operation of step 1) is: and setting a fixed sampling time length on each off-line reference point, and continuously acquiring the received signal strength of each AP in the environment at a fixed frequency to obtain a received signal strength sample so as to form an off-line fingerprint.
3. The normal distribution signal-based fingerprint optimization method according to claim 1, wherein the specific operation of step 2) is:
a1) setting the received signal strength sample collected by each AP on the off-line reference point as rssi1,rssi2,...,rssinRespectively calculating the average value of the received signal strength samples collected by each AP
And variance
Wherein, a threshold omega is set again1Then eliminate the variance σ2>Ω1A corresponding AP;
a2) constructing a mean sequence by the mean values corresponding to the residual APs after the elimination in the step a1)
Obtaining the mean value of the mean value sequence
And variance
Wherein N is the number of off-line reference points with corresponding AP, and then a threshold omega is set2Then eliminate the variance
The corresponding AP.
4. The normal distribution signal-based fingerprint optimization method according to claim 1, wherein the specific operation of step 3) is: and collecting received signal strength samples of all APs in the environment at any moment on all online test points to form single online fingerprints.
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| CN101873607B (en) * | 2010-06-25 | 2012-10-03 | 哈尔滨工业大学 | WLAN (Wireless Local Area Network) indoor step-type RD-ANFIS (Region Division-Adaptive Network-based Fuzzy Inference System) positioning method |
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| CN104869641B (en) * | 2015-06-18 | 2018-04-03 | 重庆大学 | Wi Fi indoor orientation methods based on AP optimizations |
| CN105282758A (en) * | 2015-09-06 | 2016-01-27 | 华南理工大学 | Self-adaptive dynamic construction method of WIFI indoor positioning system fingerprint database |
| CN105933975A (en) * | 2016-04-11 | 2016-09-07 | 南京邮电大学 | WiFi fingerprint-based accuracy improved indoor positioning method |
| CN105792356A (en) * | 2016-04-22 | 2016-07-20 | 西安理工大学 | A location fingerprint positioning method based on wifi |
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