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CN115079089A - Differential correction-based non-cooperative unmanned aerial vehicle accurate positioning method and device - Google Patents

Differential correction-based non-cooperative unmanned aerial vehicle accurate positioning method and device Download PDF

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CN115079089A
CN115079089A CN202210654153.2A CN202210654153A CN115079089A CN 115079089 A CN115079089 A CN 115079089A CN 202210654153 A CN202210654153 A CN 202210654153A CN 115079089 A CN115079089 A CN 115079089A
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刘昕
孙胜
宫法明
吴春雷
杨大伟
赵庆齐
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China University of Petroleum East China
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Abstract

The invention provides a differential correction-based non-cooperative unmanned aerial vehicle accurate positioning method and device. The method comprises the steps of defining 3-dimensional distance difference of correction points to construct a distance difference space model; acquiring self-flying data of the unmanned aerial vehicle of our party in advance, acquiring the precise position of the unmanned aerial vehicle and the TDOA distance difference, and realizing systematic positioning correction of a distance difference space model; after the invasion of the non-cooperative unmanned aerial vehicle is discovered, the precise position of the non-cooperative unmanned aerial vehicle is corrected in real time through the accompanying flight of the unmanned aerial vehicle of one party, a flight sequence sample set is constructed by using collected self-flight data and flight data of the non-cooperative unmanned aerial vehicle, a GRU sequence prediction model is constructed, the unmanned aerial vehicle self-flight sequence sample carrying a soft module of one party is used as a training set, the GRU sequence prediction model is trained, the flight sequence of the non-cooperative unmanned aerial vehicle is used as a prediction set, the decision-making level fusion is carried out on the prediction result of the GRU model and the assimilation positioning result corrected in real time, and the precise coordinate of final positioning is obtained.

Description

一种基于差分校正的非合作无人机精确定位方法与装置A method and device for precise positioning of non-cooperative unmanned aerial vehicles based on differential correction

技术领域technical field

本发明涉及一种非合作无人机精确定位方法，特别涉及一种基于差分校正的非合作无人机精确定位方法。The invention relates to a precise positioning method of a non-cooperative unmanned aerial vehicle, in particular to a precise positioning method of a non-cooperative unmanned aerial vehicle based on differential correction.

背景技术Background technique

近年来，由于无人机具有机动灵活、功能丰富、易操作等特点，在战场、航拍、农业、测绘、救灾等军用、民用以及工业领域被广泛使用，无人机市场呈爆发式增长。在无人机数量快速增长的同时，无人机违规飞行、无人机袭击事件也不断发生，因此无人机防御受到了广泛的关注。实现对非合作无人机的无源精确定位，是无人机防御的必要措施，但是受天气、环境、干扰波的影响，传统定位方法对非合作无人机定位误差较大，如何在防御空间环境下，应用现有技术基础降低定位误差，实现对非合作无人机的无源精确定位是目前研究的热点问题。In recent years, due to the characteristics of flexibility, rich functions, and easy operation, UAVs have been widely used in military, civil and industrial fields such as battlefields, aerial photography, agriculture, surveying and mapping, and disaster relief. The UAV market has experienced explosive growth. While the number of drones is growing rapidly, drones flying illegally and drone attacks continue to occur, so drone defense has received widespread attention. Achieving passive precise positioning of non-cooperative UAVs is a necessary measure for UAV defense. However, due to the influence of weather, environment, and interference waves, traditional positioning methods have large positioning errors for non-cooperative UAVs. In the space environment, applying the existing technology to reduce the positioning error and realizing the passive precise positioning of the non-cooperative UAV is a hot research issue at present.

针对无人机无源定位问题，通常使用天线阵列接收无人机与地面站或者遥控器通信的信号，并通过阵列信号处理算法定位无人机，作用距离可达3km，能够满足大部分场合需求。多站无源定位借助一些信息(时差，角度等)来确定多个定位曲线(或曲面)，通过求解曲线(或曲面)的交点即可得到目标位置。目前已有成熟的3种主流方法：基于信号强度指示(RSSI) 定位，依据信号发射源到接收端之间的信号衰减模型，确定两端的距离，进而通过多接收点定位；基于信号到达时间差(TDOA)定位，通过比较信号从发射源到达各个接收端之间的绝对时间差，并将其换算为距离差，计算信号源的位置；基于波达方向估计(DOA)定位，根据信号从信号源入射到阵列天线的相位差来估计信号源的方位。由于无人机信号受到环境和其他信号的干扰，目前无源定位的误差最小为20m，难以达到精确打击与反制的需要。For the passive positioning of UAVs, the antenna array is usually used to receive the signals that the UAV communicates with the ground station or the remote control, and the UAV is located through the array signal processing algorithm. The working distance can reach 3km, which can meet the needs of most occasions. . Multi-station passive positioning uses some information (time difference, angle, etc.) to determine multiple positioning curves (or surfaces), and the target position can be obtained by solving the intersection of the curves (or surfaces). At present, there are three mature mainstream methods: positioning based on signal strength indicator (RSSI), according to the signal attenuation model between the signal transmitting source and the receiving end, to determine the distance between the two ends, and then positioning through multiple receiving points; based on the signal arrival time difference ( TDOA) positioning, by comparing the absolute time difference between the signal from the transmitting source to each receiving end, and converting it to the distance difference, the position of the signal source is calculated; based on the direction of arrival (DOA) positioning, according to the signal incident from the signal source The phase difference to the array antenna is used to estimate the azimuth of the signal source. Due to the interference of the UAV signal by the environment and other signals, the current passive positioning error is at least 20m, which is difficult to achieve the needs of precise strike and countermeasures.

基于上述研究所存在的问题，本发明提出了一种基于差分校正的非合作无人机精确定位方法与装置。本发明通过定义校正点的3维距离差分，构建距离差分空间模型；事先采集我方无人机自飞数据，获取无人机精确位置和TDOA距离差分，实现距离差分空间模型的系统性定位校正；发现非合作无人机入侵后，通过我方无人机伴飞，实时校正非合作无人机精确位置，实现无人机精确定位。Based on the problems existing in the above researches, the present invention proposes a method and device for precise positioning of non-cooperative UAVs based on differential correction. The invention constructs the distance difference space model by defining the 3-dimensional distance difference of the correction point; collects the self-flying data of our UAV in advance, obtains the UAV's precise position and the TDOA distance difference, and realizes the systematic positioning correction of the distance difference space model ; After discovering the invasion of non-cooperative drones, use our drones to accompany them to correct the precise positions of non-cooperative drones in real time to achieve precise positioning of the drones.

发明内容SUMMARY OF THE INVENTION

为了解决现有技术问题，本发明提供了一种基于差分校正的非合作无人机精确定位方法与装置。本发明在防御区域内，按照特定空间间隔设置经纬度坐标校正点，形成空间网格，定义校正点6维度空间位置表示，用于精确解算三维坐标及校正点的3维差分数据，从而构建出包含所有校正点的空间差分模型。针对选定的防御区域，安排我方无人机提前试飞并回传精确定位数据，根据无人机精确位置与防御系统TDOA定位数据的空间差值，解算校正点 3维差分值，并建立空间模型校正及差分定位数据库，实现系统性空间位置模型的标定。应用校正后的系统性空间模型，研究事前静态校正与防御现场实时差分相结合的精确定位方案，提升对非合作无人机的定位精度。In order to solve the problems of the prior art, the present invention provides a method and device for precise positioning of a non-cooperative UAV based on differential correction. In the invention, in the defense area, the latitude and longitude coordinate correction points are set according to specific space intervals to form a spatial grid, and the six-dimensional spatial position representation of the correction point is defined, which is used to accurately calculate the three-dimensional coordinates and the three-dimensional difference data of the correction point, thereby constructing a Spatial difference model containing all calibration points. For the selected defense area, arrange our UAV to test flight in advance and send back precise positioning data. According to the spatial difference between the precise position of the UAV and the TDOA positioning data of the defense system, the 3-dimensional difference value of the correction point is calculated and established. Spatial model correction and differential positioning database to achieve systematic spatial position model calibration. Apply the corrected systematic space model to study the precise positioning scheme that combines the static correction in advance and the real-time difference of the defense site to improve the positioning accuracy of non-cooperative UAVs.

本发明所采用的技术方案如下：The technical scheme adopted in the present invention is as follows:

一种基于差分校正的非合作无人机精确定位方法与装置，包括以下步骤：A method and device for precise positioning of a non-cooperative UAV based on differential correction, comprising the following steps:

A、构建距离差分空间模型。设定分布式时差定位空间范围，在空间坐标系中每个方向间隔1m设置一个坐标点，称为校正点，用精确的经纬度和高度标记其位置坐标，所有校正点构成一个空间网格。因为空间环境的差异和各类信号的干扰，TDOA定位系统给出的无人机坐标在每个位置上存在不同的误差，为每一个校正点增设3个距离差分维度，构成6维位置坐标，第i个校正点位置坐标为

Figure RE-GDA0003754407570000021
用于存放其精确3维坐标和解算出的校正点3维差分数据，校正空间中间隔1m的所有6维坐标点形成了距离差分空间模型。A. Build a distance difference space model. Set the spatial range of distributed time difference positioning, set a coordinate point at an interval of 1m in each direction in the spatial coordinate system, called a correction point, mark its position coordinates with precise latitude, longitude and height, and all correction points form a spatial grid. Due to the difference in the space environment and the interference of various signals, the coordinates of the UAV given by the TDOA positioning system have different errors at each position. For each correction point, 3 distance difference dimensions are added to form 6-dimensional position coordinates. The position coordinates of the i-th calibration point are
Figure RE-GDA0003754407570000021
It is used to store its precise 3D coordinates and the calculated 3D difference data of calibration points. All 6D coordinate points in the calibration space with an interval of 1m form a distance difference space model.

B、基于差分的系统性空间模型定位校正。在防御空间释放我方携带软模块的无人机自飞，飞行过程中回传自身GPS精确位置，通过多轮自飞使飞行轨迹覆盖距离差分空间模型的每个网格。针对空间模型中每个校正点，获取其周围4个网格中回传的多个精确位置和每个位置相应的TDOA坐标，分别记为P1，...，Pi，...，Pn和P′1，...，P′i，...，P′n，其中Pi的精确位置标记为

Figure RE-GDA0003754407570000022
TDOA系统定位的位置P′i记为(x′i，y′i，z′i)。基于精确位置和TDOA系统坐标，根据公式(1)-(3)计算网格中间定位点的3维距离差分，分别记为dx，dy，dz。B. Systematic spatial model positioning correction based on difference. In the defensive space, we release our UAV carrying the soft module for self-flying, and return its precise GPS position during the flight. Through multiple rounds of self-flying, the flight trajectory covers each grid of the distance difference space model. For each calibration point in the space model, obtain multiple accurate positions returned in four grids around it and the corresponding TDOA coordinates of each position, which are respectively recorded as P 1 ,..., P i ,..., Pn and P'1 ,..., P'i,...,P'n , where the exact position of Pi is marked as
Figure RE-GDA0003754407570000022
The position P' i located by the TDOA system is denoted as (x' i , y' i , z' i ). Based on the precise position and the TDOA system coordinates, the 3-dimensional distance difference of the grid intermediate positioning point is calculated according to formulas (1)-(3), which are respectively recorded as d x , dy , and d z .

Figure RE-GDA0003754407570000023
Figure RE-GDA0003754407570000023

Figure RE-GDA0003754407570000024
Figure RE-GDA0003754407570000024

Figure RE-GDA0003754407570000025
Figure RE-GDA0003754407570000025

根据计算得到每一个校正点的dx，dy，dz，更新距离差分空间模型中每一个校正点距离差分值，实现整个空间模型系统性定位校正。According to the d x , dy , d z of each correction point obtained by calculation, the distance difference value of each correction point in the distance difference space model is updated to realize the systematic positioning correction of the whole space model.

C、基于实时校正的非合作无人机精确定位。在TDOA系统侦测到入侵无人机信号时，计算入侵无人机的实时TDOA位置，作为初始位置P0(x′0，y′0，z′0)。将其放入距离差分空间模型中，若该位置落在校正点上，则根据校正点当前的3维距离差分值直接计算非合作无人机的校正位置

Figure RE-GDA0003754407570000031
若该位置落在非校正点，取位置所在网格的4个校正点的3维距离差分值，根据公(4)-(6)计算该位置的距离差分，获得该位置的系统静态校正位置
Figure RE-GDA0003754407570000032
C. Precise positioning of non-cooperative UAVs based on real-time correction. When the TDOA system detects the signal of the intruding UAV, the real-time TDOA position of the intruding UAV is calculated as the initial position P 0 (x′ 0 , y′ 0 , z′ 0 ). Put it into the distance difference space model, if the position falls on the correction point, then directly calculate the correction position of the non-cooperative UAV according to the current 3D distance difference value of the correction point
Figure RE-GDA0003754407570000031
If the position falls on a non-calibration point, take the 3-dimensional distance difference value of the 4 calibration points in the grid where the position is located, calculate the distance difference of the position according to public (4)-(6), and obtain the system static calibration position of the position.
Figure RE-GDA0003754407570000032

Figure RE-GDA0003754407570000033
Figure RE-GDA0003754407570000033

Figure RE-GDA0003754407570000034
Figure RE-GDA0003754407570000034

Figure RE-GDA0003754407570000035
Figure RE-GDA0003754407570000035

根据系统静态校正位置，释放我方无人机伴飞，接近非合作无人机时(在30cm范围内时定义为交叉点)，我方无人机回传自身精确GPS位置，计算我方无人机的精确位置与系统静态校正位置的距离差分，作为非合作无人机的距离差分，实时校正非合作无人机精确位置。在伴飞过程中我方无人机有多次与非合作无人机接近，将每一个交叉点作为一个同化点，实现对非合作无人机持续实时更新同化定位。用收集的自飞数据和非合作无人机飞行数据构建飞行序列样本集，每一个样本表示为(TDOA位置，精确位置)。构建GRU序列预测模型，用我方携带软模块的无人机自飞序列样本作为训练集，训练GRU序列预测模型，将非合作无人机的飞行序列作为预测集，将GRU模型的预测结果与实时校正后的同化定位结果进行决策级融合，得到最终定位的精确坐标。According to the static correction position of the system, release our UAV to accompany the flight, and when approaching the non-cooperative UAV (defined as the intersection point within the range of 30cm), our UAV returns its precise GPS position, and calculates the non-cooperative UAV. The distance difference between the precise position of the man-machine and the static correction position of the system is used as the distance difference of the non-cooperative UAV to correct the precise position of the non-cooperative UAV in real time. During the accompanying flight, our drones approached non-cooperative drones many times, and each intersection was used as an assimilation point to continuously update the assimilation positioning of non-cooperative drones in real time. A sample set of flight sequences is constructed from the collected self-flying data and non-cooperative UAV flight data, and each sample is denoted as (TDOA position, precise position). Build a GRU sequence prediction model, use our UAV self-flying sequence samples with soft modules as the training set, train the GRU sequence prediction model, take the flight sequences of non-cooperative UAVs as the prediction set, and compare the prediction results of the GRU model with the prediction results of the GRU model. The assimilation positioning results after real-time correction are fused at the decision level to obtain the precise coordinates of the final positioning.

步骤A中TDOA定位系统是一种利用到达时间差进行定位的方法。每次定位标签发射定位广播信号，通过此信号到达每个定位基站之间的时间差值及所有基站的已知位置等信息，计算出标签的坐标。In step A, the TDOA positioning system is a method for positioning using the time difference of arrival. Each time the positioning tag transmits a positioning broadcast signal, the coordinates of the tag are calculated based on information such as the time difference between the signal reaching each positioning base station and the known positions of all base stations.

步骤B中的软模块是用于嵌入无人机身份信息的固件编程代码。The soft module in step B is the firmware programming code used to embed the drone's identity information.

步骤C中的GRU是一种神经网络，引入重置门和更新门的概念，通过门控制机制来控制和管理神经网络中细胞之间的信息流，记忆过去的信息，同时选择性地忘记一些不重要的信息，解决长期记忆和反向传播中的梯度问题。The GRU in step C is a kind of neural network, which introduces the concept of reset gate and update gate, controls and manages the information flow between cells in the neural network through the gate control mechanism, remembers the past information, and selectively forgets some Unimportant information, solving gradient problems in long-term memory and backpropagation.

另一方面，本发明提供了一种基于差分校正的非合作无人机精确定位装置，包括以下模块：On the other hand, the present invention provides a non-cooperative UAV precise positioning device based on differential correction, comprising the following modules:

距离差分空间模型构建模块：设定分布式时差定位空间范围，在空间坐标系中每个方向间隔1m设置一个坐标点，称为校正点，用精确的经纬度和高度标记其位置坐标，所有校正点构成一个空间网格。为每一个校正点增设3个距离差分维度，构成6维位置坐标，第i个校正点位置坐标为

Figure RE-GDA0003754407570000036
用于存放其精确3维坐标和解算出的校正点3维差分数据，校正空间中间隔1m的所有6维坐标点形成了距离差分空间模型。Distance difference space model building module: set the spatial range of distributed time difference positioning, set a coordinate point at an interval of 1m in each direction in the space coordinate system, called a correction point, mark its position coordinates with precise latitude, longitude and altitude, all correction points form a spatial grid. Add 3 distance difference dimensions for each calibration point to form 6-dimensional position coordinates, and the position coordinates of the i-th calibration point are
Figure RE-GDA0003754407570000036
It is used to store its precise 3D coordinates and the calculated 3D difference data of calibration points. All 6D coordinate points in the calibration space with an interval of 1m form a distance difference space model.

空间模型定位校正模块：针对空间模型中每个校正点，获取其周围4个网格中无人机自飞回传的多个精确位置和每个位置相应的TDOA坐标，分别记为P1，...，Pi，...，Pn和P′1，...，P′i，...，P′n，其中Pi的精确位置标记为

Figure RE-GDA0003754407570000041
TDOA系统定位的位置P′i记为(x′i，y′i，z′i)。根据公式(1)-(3)计算网格中间定位点的3维距离差分，分别记为dx，dy，dz。根据计算得到每一个校正点的dx，dy，dz，更新距离差分空间模型中每一个校正点距离差分值，实现整个空间模型系统性定位校正。Spatial model positioning and correction module: For each correction point in the space model, obtain multiple precise positions of the UAV self-flying back and the corresponding TDOA coordinates of each position in the four surrounding grids, which are respectively recorded as P 1 , ..., Pi,..., Pn and P'1 ,..., P'i ,..., P'n , where the exact position of Pi is marked as
Figure RE-GDA0003754407570000041
The position P' i located by the TDOA system is denoted as (x' i , y' i , z' i ). According to formulas (1)-(3), the 3-dimensional distance difference of the positioning point in the middle of the grid is calculated, which are respectively recorded as d x , dy , and d z . According to the d x , dy , d z of each correction point obtained by calculation, the distance difference value of each correction point in the distance difference space model is updated to realize the systematic positioning correction of the whole space model.

非合作无人机精确定位模块：在TDOA系统侦测到入侵无人机信号时，计算入侵无人机的实时TDOA位置，作为初始位置P0(x′0，y′0，z′0)。将其放入距离差分空间模型中，若该位置落在校正点上，则根据校正点当前的3维距离差分值直接计算非合作无人机的校正位置

Figure RE-GDA0003754407570000042
若该位置落在非校正点，取位置所在网格的4个校正点的3维距离差分值，根据公(4)-(6)计算该位置的距离差分，获得该位置的系统静态校正位置
Figure RE-GDA0003754407570000043
根据系统静态校正位置，释放我方无人机伴飞，接近非合作无人机时(在30cm范围内时定义为交叉点)，我方无人机回传自身精确GPS位置，计算我方无人机的精确位置与系统静态校正位置的距离差分，作为非合作无人机的距离差分，实时校正非合作无人机精确位置。在伴飞过程中我方无人机有多次与非合作无人机接近，将每一个交叉点作为一个同化点，实现对非合作无人机持续实时更新同化定位。用收集的自飞数据和非合作无人机飞行数据构建飞行序列样本集，每一个样本表示为(TDOA位置，精确位置)。构建GRU序列预测模型，用我方携带软模块的无人机自飞序列样本作为训练集，训练GRU 序列预测模型，将非合作无人机的飞行序列作为预测集，将GRU模型的预测结果与实时校正后的同化定位结果进行决策级融合，得到最终定位的精确坐标。Non-cooperative UAV precise positioning module: when the TDOA system detects the signal of the intruding UAV, calculate the real-time TDOA position of the intruding UAV as the initial position P 0 (x′ 0 , y′ 0 , z′ 0 ) . Put it into the distance difference space model, if the position falls on the correction point, then directly calculate the correction position of the non-cooperative UAV according to the current 3D distance difference value of the correction point
Figure RE-GDA0003754407570000042
If the position falls on a non-calibration point, take the 3-dimensional distance difference value of the 4 calibration points in the grid where the position is located, calculate the distance difference of the position according to public (4)-(6), and obtain the system static calibration position of the position.
Figure RE-GDA0003754407570000043
According to the static correction position of the system, release our UAV to accompany the flight, and when approaching the non-cooperative UAV (defined as the intersection point within the range of 30cm), our UAV returns its precise GPS position, and calculates the non-cooperative UAV. The distance difference between the precise position of the man-machine and the static correction position of the system is used as the distance difference of the non-cooperative UAV to correct the precise position of the non-cooperative UAV in real time. During the accompanying flight, our drones approached non-cooperative drones many times, and each intersection was used as an assimilation point to continuously update the assimilation positioning of non-cooperative drones in real time. A sample set of flight sequences is constructed from the collected self-flying data and non-cooperative UAV flight data, and each sample is denoted as (TDOA position, precise position). Build a GRU sequence prediction model, use our UAV self-flying sequence samples with soft modules as the training set, train the GRU sequence prediction model, take the flight sequence of non-cooperative UAVs as the prediction set, and compare the prediction results of the GRU model with the prediction results of the GRU model. The assimilation positioning results corrected in real time are fused at the decision level to obtain the precise coordinates of the final positioning.

附图说明Description of drawings

为了更清楚的说明本发明实施例中的技术方案，下面将对实施例描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。In order to illustrate the technical solutions in the embodiments of the present invention more clearly, the following briefly introduces the accompanying drawings used in the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained from these drawings without creative effort.

图1为本发明的一种基于差分校正的非合作无人机精确定位方法执行流程图。FIG. 1 is an execution flow chart of a method for precise positioning of a non-cooperative UAV based on differential correction of the present invention.

图2为本发明的基于距离差分的系统性空间模型定位校正图。FIG. 2 is a positioning correction diagram of a systematic spatial model based on distance difference according to the present invention.

图3为本发明的基于实时校正的非合作无人机位置增强图Fig. 3 is the non-cooperative UAV position enhancement map based on real-time correction of the present invention

具体实施方式Detailed ways

为使本发明的目的、技术方案和优点更加清楚，下面将结合附图对本发明实施方式作进一步地详细描述。In order to make the objectives, technical solutions and advantages of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

实施例一Example 1

本实施例的基础在于，在现有的无人机定位技术中，传统方法对非合作无人机定位误差较大，无法精确定位非合作无人机位置进行无人机防御。因此，我们通过构建距离差分空间模型，通过静态系统更新和动态实时更新定位点距离差分，精确定位非合作无人机位置。The basis of this embodiment is that, in the existing UAV positioning technology, the traditional method has a large positioning error for the non-cooperative UAV, and cannot precisely locate the position of the non-cooperative UAV for UAV defense. Therefore, we precisely locate the position of the non-cooperative UAV by constructing a distance difference space model, through static system update and dynamic real-time update of the positioning point distance difference.

首先设定分布式时差定位空间范围，在空间坐标系中每个方向间隔1m设置一个坐标点，称为校正点，用精确的经纬度和高度标记其位置坐标，所有校正点构成一个空间网格。因为空间环境的差异和各类信号的干扰，TDOA定位系统给出的无人机坐标在每个位置上存在不同的误差，为每一个校正点增设3个距离差分维度，构成6维位置坐标，第i个校正点位置坐标为

Figure RE-GDA0003754407570000051
用于存放其精确3维坐标和解算出的校正点3维差分数据，校正空间中间隔1m的所有6维坐标点形成了距离差分空间模型。First, set the spatial range of distributed time difference positioning, and set a coordinate point at an interval of 1m in each direction in the spatial coordinate system, which is called a correction point, and mark its position coordinates with precise longitude, latitude and height. All correction points form a spatial grid. Due to the difference in the space environment and the interference of various signals, the coordinates of the UAV given by the TDOA positioning system have different errors at each position. For each correction point, 3 distance difference dimensions are added to form 6-dimensional position coordinates. The position coordinates of the i-th calibration point are
Figure RE-GDA0003754407570000051
It is used to store its precise 3D coordinates and the calculated 3D difference data of calibration points. All 6D coordinate points in the calibration space with an interval of 1m form a distance difference space model.

在防御空间释放我方携带软模块的无人机自飞，飞行过程中回传自身GPS精确位置，通过多轮自飞使飞行轨迹覆盖距离差分空间模型的每个网格。针对空间模型中每个校正点，获取其周围4个网格中回传的多个精确位置和每个位置相应的TDOA坐标，分别记为P1，...，Pi，...，Pn和P′1，...，P′i，...，P′n，其中Pi的精确位置标记为

Figure RE-GDA0003754407570000052
TDOA系统定位的位置P′i记为(x′i，y′i，z′i)。基于精确位置和TDOA系统坐标，根据公式(1)-(3)计算网格中间定位点的3维距离差分，分别记为dx，dy，dz。根据计算得到每一个校正点的dx，dy，dz，更新距离差分空间模型中每一个校正点距离差分值，实现整个空间模型系统性定位校正。In the defensive space, we release our UAV carrying the soft module for self-flying, and return its precise GPS position during the flight. Through multiple rounds of self-flying, the flight trajectory covers each grid of the distance difference space model. For each calibration point in the space model, obtain multiple accurate positions returned in four grids around it and the corresponding TDOA coordinates of each position, which are respectively recorded as P 1 ,..., P i ,..., Pn and P'1 ,..., P'i,...,P'n , where the exact position of Pi is marked as
Figure RE-GDA0003754407570000052
The position P' i located by the TDOA system is denoted as (x' i , y' i , z' i ). Based on the precise position and the TDOA system coordinates, the 3-dimensional distance difference of the grid intermediate positioning point is calculated according to formulas (1)-(3), which are respectively recorded as d x , dy , and d z . According to the d x , dy , d z of each correction point obtained by calculation, the distance difference value of each correction point in the distance difference space model is updated to realize the systematic positioning correction of the whole space model.

在TDOA系统侦测到入侵无人机信号时，计算入侵无人机的实时TDOA位置，作为初始位置P0(x′0，y′0，z′0)。将其放入距离差分空间模型中，若该位置落在校正点上，则根据校正点当前的3维距离差分值直接计算非合作无人机的校正位置

Figure RE-GDA0003754407570000053
若该位置落在非校正点，取位置所在网格的4个校正点的3维距离差分值，根据公(4)-(6)计算该位置的距离差分，获得该位置的系统静态校正位置
Figure RE-GDA0003754407570000054
根据系统静态校正位置，释放我方无人机伴飞，接近非合作无人机时，我方无人机回传自身精确GPS位置，计算我方无人机的精确位置与系统静态校正位置的距离差分，作为非合作无人机的距离差分，实时校正非合作无人机精确位置。在伴飞过程中我方无人机有多次与非合作无人机接近，将每一个交叉点作为一个同化点，实现对非合作无人机持续实时更新同化定位。用收集的自飞数据和非合作无人机飞行数据构建飞行序列样本集，每一个样本表示为(TDOA位置，精确位置)。构建GRU序列预测模型，用我方携带软模块的无人机自飞序列样本作为训练集，训练GRU序列预测模型，将非合作无人机的飞行序列作为预测集，将GRU模型的预测结果与实时校正后的同化定位结果进行决策级融合，得到最终定位的精确坐标。When the TDOA system detects the signal of the intruding UAV, the real-time TDOA position of the intruding UAV is calculated as the initial position P 0 (x′ 0 , y′ 0 , z′ 0 ). Put it into the distance difference space model, if the position falls on the correction point, then directly calculate the correction position of the non-cooperative UAV according to the current 3D distance difference value of the correction point
Figure RE-GDA0003754407570000053
If the position falls on a non-calibration point, take the 3-dimensional distance difference value of the 4 calibration points in the grid where the position is located, calculate the distance difference of the position according to public (4)-(6), and obtain the system static calibration position of the position.
Figure RE-GDA0003754407570000054
According to the static correction position of the system, release our UAV to accompany the flight. When approaching the non-cooperative UAV, our UAV sends back its own precise GPS position, and calculates the difference between the precise position of our UAV and the static correction position of the system. The distance difference, as the distance difference of the non-cooperative UAV, corrects the precise position of the non-cooperative UAV in real time. During the accompanying flight, our drones approached non-cooperative drones many times, and each intersection was used as an assimilation point to continuously update the assimilation positioning of non-cooperative drones in real time. A sample set of flight sequences is constructed from the collected self-flying data and non-cooperative UAV flight data, and each sample is denoted as (TDOA position, precise position). Build a GRU sequence prediction model, use our UAV self-flying sequence samples with soft modules as the training set, train the GRU sequence prediction model, take the flight sequences of non-cooperative UAVs as the prediction set, and compare the prediction results of the GRU model with the prediction results of the GRU model. The assimilation positioning results corrected in real time are fused at the decision level to obtain the precise coordinates of the final positioning.

Claims (5)

1. A non-cooperative unmanned aerial vehicle accurate positioning method based on differential correction comprises the following steps:
A. and constructing a distance difference space model. Setting a distributed time difference positioning space range, setting a coordinate point, called a correction point, in each direction at an interval of 1m in a space coordinate system, marking the position coordinates of the coordinate points by using accurate longitude and latitude and height, and forming a space grid by all the correction points. Because of the difference of space environment and the interference of various signals, the coordinate of the unmanned aerial vehicle given by the TDOA positioning system has different errors at each position, 3 distance difference dimensions are added for each correction point to form 6-dimensional position coordinates, and the position coordinate of the ith correction point is
Figure FDA0003688541220000011
The distance difference model is used for storing accurate 3-dimensional coordinates and 3-dimensional difference data of calculated correction points, and all 6-dimensional coordinate points at intervals of 1m in a correction space form a distance difference space model.
B. A differential-based systematic spatial model location correction. The unmanned aerial vehicle carrying the soft module in one part is released to fly automatically in the defense space, the GPS accurate position of the unmanned aerial vehicle is returned in the flying process, and the flying track covers each grid of the distance difference space model through multiple rounds of self-flying. Aiming at each correction point in the space model, acquiring a plurality of accurate positions returned in 4 grids around the correction point and TDOA coordinates corresponding to each position, and respectively marking as P 1 ，...，P i ，...，P n And P' 1 ，...，P′ i ，...，P′ n In which P is i Is marked as an accurate position
Figure FDA0003688541220000012
Position P 'of TDOA system location' i Is recorded as (x' i ，y′ i ，z′ i ). Computing a grid based on the precise location and TDOA system coordinates according to equations (1) - (3)The 3-dimensional distance difference of the middle positioning point is respectively marked as d x ，d y ，d z 。
Figure FDA0003688541220000013
Figure FDA0003688541220000014
Figure FDA0003688541220000015
D of each correction point is obtained according to calculation x ，d y ，d z And updating the distance difference value of each correction point in the distance difference space model to realize systematic positioning correction of the whole space model.
C. Non-cooperative unmanned aerial vehicle accurate positioning based on real-time correction. When the TDOA system detects the signal of the invading unmanned aerial vehicle, the real-time TDOA position of the invading unmanned aerial vehicle is calculated and taken as the initial position P 0 (x′ 0 ，y′ 0 ，z′ 0 ). Putting the unmanned aerial vehicle into a distance difference space model, and if the position falls on a correction point, directly calculating the correction position of the non-cooperative unmanned aerial vehicle according to the current 3-dimensional distance difference value of the correction point
Figure FDA0003688541220000016
If the position is located at the non-correction point, taking the 3-dimensional distance difference value of 4 correction points of the grid where the position is located, calculating the distance difference of the position according to the formulas (4) - (6), and obtaining the system static correction position of the position
Figure FDA0003688541220000017
Figure FDA0003688541220000018
Figure FDA0003688541220000019
Figure FDA0003688541220000021
And releasing the unmanned aerial vehicle of the owner to fly together according to the static correction position of the system, returning the self accurate GPS position by the unmanned aerial vehicle of the owner when the unmanned aerial vehicle is close to the non-cooperative unmanned aerial vehicle (defined as a cross point within the range of 30 cm), calculating the distance difference between the accurate position of the unmanned aerial vehicle of the owner and the static correction position of the system, taking the distance difference as the distance difference of the non-cooperative unmanned aerial vehicle, and correcting the accurate position of the non-cooperative unmanned aerial vehicle in real time. In the accompanying flight process, the unmanned aerial vehicle of one party is close to the non-cooperative unmanned aerial vehicle for many times, and each intersection point is used as an assimilation point, so that the non-cooperative unmanned aerial vehicle can be continuously updated and positioned in real time. A flight sequence sample set is constructed with the collected self-flight data and non-cooperative drone flight data, each sample being represented as (TDOA location, precise location). And (3) constructing a GRU sequence prediction model, taking an unmanned aerial vehicle self-flying sequence sample carrying a soft module in one party as a training set, training the GRU sequence prediction model, taking a flying sequence of a non-cooperative unmanned aerial vehicle as a prediction set, and performing decision-level fusion on a prediction result of the GRU model and an assimilation positioning result corrected in real time to obtain accurate coordinates of final positioning.
2. The method as claimed in claim 1, wherein in step a, the TDOA is a method of using time difference of arrival to perform positioning. The distance of the signal source is determined by measuring the time of arrival of the signal at the monitoring station.
3. The method for accurately positioning a non-cooperative drone based on differential correction as claimed in claim 1, wherein in step B, the soft module is firmware programming code for embedding identity information of the drone.
4. The method as claimed in claim 1, wherein in step C, the GRU is a neural network, the concept of reset gate and refresh gate is introduced, the gate control mechanism is used to control and manage the information flow between cells in the neural network, and the past information is memorized while some unimportant information is selectively forgotten, so as to solve the problem of gradient in long-term memory and back propagation.
5. The invention provides a differential correction-based non-cooperative unmanned aerial vehicle accurate positioning device, which comprises the following modules:
the distance difference space model construction module comprises: setting a distributed time difference positioning space range, setting a coordinate point, called a correction point, in each direction at an interval of 1m in a space coordinate system, marking the position coordinates of the coordinate points by using accurate longitude and latitude and height, and forming a space grid by all the correction points. Adding 3 distance difference dimensions for each correction point to form 6-dimensional position coordinates, wherein the position coordinate of the ith correction point is
Figure FDA0003688541220000022
The distance difference model is used for storing accurate 3-dimensional coordinates and 3-dimensional difference data of calculated correction points, and all 6-dimensional coordinate points at intervals of 1m in a correction space form a distance difference space model.
The space model positioning correction module: aiming at each correction point in the space model, acquiring a plurality of accurate positions of self-flying return of the unmanned aerial vehicle in 4 grids around the correction point and TDOA coordinates corresponding to each position, and respectively recording as P 1 ，...，P i ，...，P n And P' 1 ，...，P′ i ，...，P′ n In which P is i Is marked as an accurate position
Figure FDA0003688541220000023
Location P 'of TDOA System location' i Is recorded as (x' i ，y′ i ，z′ i ). Calculating the 3-dimensional distance difference of the middle positioning points of the grid according to the formulas (1) to (3), and respectively recording the 3-dimensional distance difference as d x ，d y ，d z . D of each correction point is obtained according to calculation x ，d y ，d z And updating the distance difference value of each correction point in the distance difference space model to realize systematic positioning correction of the whole space model.
Non-cooperative unmanned aerial vehicle accurate positioning module: when the TDOA system detects the signal of the invading unmanned aerial vehicle, the real-time TDOA position of the invading unmanned aerial vehicle is calculated and taken as the initial position P 0 (x′ 0 ，y′ 0 ，z′ 0 ). Putting the unmanned aerial vehicle into a distance difference space model, and if the position falls on a correction point, directly calculating the correction position of the non-cooperative unmanned aerial vehicle according to the current 3-dimensional distance difference value of the correction point
Figure FDA0003688541220000031
If the position is located at the non-correction point, taking the 3-dimensional distance difference value of 4 correction points of the grid where the position is located, calculating the distance difference of the position according to the formulas (4) - (6), and obtaining the system static correction position of the position
Figure FDA0003688541220000032
And releasing the unmanned aerial vehicle of the owner to fly together according to the static correction position of the system, returning the self accurate GPS position by the unmanned aerial vehicle of the owner when the unmanned aerial vehicle is close to the non-cooperative unmanned aerial vehicle (defined as a cross point within the range of 30 cm), calculating the distance difference between the accurate position of the unmanned aerial vehicle of the owner and the static correction position of the system, taking the distance difference as the distance difference of the non-cooperative unmanned aerial vehicle, and correcting the accurate position of the non-cooperative unmanned aerial vehicle in real time. In the accompanying flight process, the unmanned aerial vehicle of one party is close to the non-cooperative unmanned aerial vehicle for many times, and each intersection point is used as an assimilation point, so that the non-cooperative unmanned aerial vehicle can be continuously updated and positioned in real time. A flight sequence sample set is constructed with the collected self-flight data and non-cooperative drone flight data, each sample being represented as (TDOA location, precise location). Constructing a GRU sequence prediction model, and training GRU sequence prediction by using unmanned aerial vehicle self-flying sequence samples carrying soft modules of one party as a training setAnd the model takes the flight sequence of the non-cooperative unmanned aerial vehicle as a prediction set, and carries out decision-making fusion on the prediction result of the GRU model and the assimilation positioning result corrected in real time to obtain the accurate coordinate of final positioning.
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CN116561530A (en) * 2023-05-26 2023-08-08 深圳大漠大智控技术有限公司 Unmanned aerial vehicle flight data analysis method, device, equipment and medium
CN116561530B (en) * 2023-05-26 2024-01-26 深圳大漠大智控技术有限公司 Unmanned aerial vehicle flight data analysis method, device, equipment and medium

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