CN110221246A - A kind of unmanned plane localization method based on the fusion of multi-source direction finding message - Google Patents
- ️Tue Sep 10 2019
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- CN110221246A CN110221246A CN201910420588.9A CN201910420588A CN110221246A CN 110221246 A CN110221246 A CN 110221246A CN 201910420588 A CN201910420588 A CN 201910420588A CN 110221246 A CN110221246 A CN 110221246A Authority
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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
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
- G01S5/0278—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves involving statistical or probabilistic considerations
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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
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
- G01S5/04—Position of source determined by a plurality of spaced direction-finders
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- Radar, Positioning & Navigation (AREA)
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- Position Fixing By Use Of Radio Waves (AREA)
Abstract
本发明公开了一种基于多源测向信息融合的无人机定位方法，通过部署在不同探测位置的站点获取无人机测向信息，无人机测向信息包括无人机信号强度、信干噪比和无人机信号的波达方向，测控中心收到至少3个站点的无人机测向信息时，根据任意两个站点测量到的波达方向和站点位置，对目标无人机位置进行初步估计，然后再结合站点发送的信干噪比，对计算的所有目标无人机的位置进行线性加权求和，来获得目标无人机的最终位置。本发明提供了一种实用、快速的无人机定位方法，充分利用已有的测量信息，考虑到不同基站的位置信息以及接收信号的信干噪比，实现对目标无人机位置的精确估计，并且计算目标无人机位置的过程简单，复杂度低。
The invention discloses a UAV positioning method based on multi-source direction finding information fusion. The UAV direction finding information is obtained through sites deployed in different detection positions. The interference-to-noise ratio and the direction of arrival of the UAV signal. When the measurement and control center receives the direction finding information of the UAV from at least 3 sites, it will detect the target UAV according to the direction of arrival and the position of the site measured by any two sites. The position is initially estimated, and then combined with the signal-to-interference-noise ratio sent by the station, a linear weighted summation of the calculated positions of all target UAVs is performed to obtain the final position of the target UAV. The invention provides a practical and fast UAV positioning method, which makes full use of the existing measurement information, and takes into account the position information of different base stations and the signal-to-interference-noise ratio of the received signal, so as to realize the accurate estimation of the position of the target UAV. , and the process of calculating the target UAV position is simple and low in complexity.
Description
技术领域technical field
本发明属于无人机信源测向定位技术领域，具体涉及一种基于多源测向信息融合的无人机定位方法。The invention belongs to the technical field of UAV signal source direction finding and positioning, in particular to a UAV positioning method based on multi-source direction finding information fusion.
背景技术Background technique
近年来，由于对无人机的监管力度不足，导致相关地区的无人机“黑飞”事件的恶化与升级，造成了不良的影响。因此，实现对重点区域非合作无人机(“黑飞”无人机)的有效控制，最为核心的一项技术涉及到对“黑飞”无人机位置的有效估计。通过实现对“黑飞”无人机位置的精确估计，可以为后续的干扰反制系统提供准确依据，最终实现对重点区域或重要活动场所公共安全的有效维护。目前，针对“黑飞”无人机定位方法的研究，是新世纪航空监管的一个新领域，也是航空工业领域的一大研究热点。In recent years, due to insufficient supervision of drones, the "black flying" incidents of drones in relevant areas have deteriorated and escalated, causing adverse effects. Therefore, to realize the effective control of non-cooperative UAVs (“Black Flying” UAVs) in key areas, the core technology involves the effective estimation of the location of “Black Flying” UAVs. By realizing the accurate estimation of the position of the "black flying" UAV, it can provide an accurate basis for the subsequent interference countermeasure system, and finally realize the effective maintenance of public safety in key areas or important activity places. At present, the research on the positioning method of "black flying" UAV is a new field of aviation supervision in the new century, and it is also a major research hotspot in the field of aviation industry.
无人机定位方法多是结合多站点所接收到的信息，结合定位算法，利用已有的信息实现对无人机位置的估计。典型的定位方法有基于DOA、基于RSSI以及基于TDOA的定位算法。Most of the UAV positioning methods are combined with the information received from multiple sites, combined with positioning algorithms, and use the existing information to estimate the position of the UAV. Typical positioning methods include DOA-based, RSSI-based and TDOA-based positioning algorithms.
如参考文件[1]基于子空间旋转变换思想提出一种改进的低复杂度波达角估计方法，降低了多重信号分选算法的计算量。参考文件[2]研究了去除地面反射波的波达角估计方法，以基于极化平面的波达角估计算法为基础，针对近地面天线受地面反射波影响从而极大地影响DOA估计的准确性问题，对如何去除地面反射波的影响进行深入研究，分别采用理想地面近似法、反射系数法和阵列抑制算法进行仿真试验，通过比较分析得出，阵列抑制算法可用于任何类型的实际地面，且无需知道实际地面参数，同时该算法具有很好的准确性，因此其应用场景不受限制，具有很好的理论研究和实际应用价值。参考文件[3]为了解决对未知干扰源定位的问题，采用基于检测概率和波达角估计的单接收机定位方法，只需一个参考节点就可以确定干扰源的位置。参考文件[4]针对现有无人机在采用单一传感器测量飞行高度时不够精确且易受干扰，而当前多传感器融合算法测量精度提升有限的问题，提出了一种多层多源信息融合旋翼无人机测高算法。For example, reference document [1] proposes an improved low-complexity angle of arrival estimation method based on the idea of subspace rotation transformation, which reduces the computational complexity of the multiple signal sorting algorithm. Reference [2] studies the method of estimating the angle of arrival by removing the ground reflected wave. Based on the estimation algorithm of the angle of arrival based on the polarization plane, the near-ground antenna is affected by the ground reflected wave, which greatly affects the accuracy of DOA estimation. To solve the problem, in-depth research was carried out on how to remove the influence of ground reflected waves. The ideal ground approximation method, the reflection coefficient method and the array suppression algorithm were used for simulation experiments. Through comparative analysis, it was concluded that the array suppression algorithm can be used in any type of actual ground, and There is no need to know the actual ground parameters, and the algorithm has good accuracy, so its application scenarios are not limited, and it has good theoretical research and practical application value. Reference [3] In order to solve the problem of locating unknown interference sources, a single-receiver positioning method based on detection probability and angle of arrival estimation is adopted, and only one reference node can determine the position of the interference source. Reference [4] proposes a multi-layer multi-source information fusion rotor for the problem that the existing UAV is inaccurate and susceptible to interference when using a single sensor to measure the flight height, and the current multi-sensor fusion algorithm has limited improvement in measurement accuracy. UAV altimetry algorithm.
对于现有的无人机定位算法而言，考虑的场景较为理想，并且多是对定位方法的性能进行定量的分析，对实际应用场景的考虑有所欠缺。但是在对重点区域进行防护时，需要考虑到信号在空间传播时的衰减特性等实际状况，目前很少有针对实际的站点环境设计高效实用的无人机定位方法。For the existing UAV positioning algorithms, the considered scenarios are ideal, and most of them are quantitative analysis of the performance of the positioning methods, and the consideration of practical application scenarios is lacking. However, when protecting key areas, it is necessary to consider the actual conditions such as the attenuation characteristics of signals during space propagation. At present, there are few efficient and practical UAV positioning methods designed for the actual site environment.
参考文献：references:
[1]闫锋刚，齐晓辉，刘帅，沈毅，金铭.基于子空间旋转变换的低复杂度波达角估计算法[J].电子与信息学报，2016，38(03)：629-634.[1] Yan Fenggang, Qi Xiaohui, Liu Shuai, Shen Yi, Jin Ming. Low-complexity angle of arrival estimation algorithm based on subspace rotation transformation [J]. Journal of Electronics and Information, 2016, 38(03): 629-634.
[2]冯瑜，纪奕才，方广有.去除地面反射波的波达角估计方法研究[J].电波科学学报，2017，32(06)：702-711.[2] Feng Yu, Ji Yicai, Fang Guangyou. Research on the method of estimating the angle of arrival of ground reflected waves [J]. Journal of Radio Wave Science, 2017, 32(06): 702-711.
[3]黄茂松，郭艳，李宁.基于检测概率和波达角估计的单接收机定位方法[J].江南大学学报(自然科学版)，2013，12(03)：258-261.[3] Huang Maosong, Guo Yan, Li Ning. Single-receiver positioning method based on detection probability and angle of arrival estimation [J]. Journal of Jiangnan University (Natural Science Edition), 2013, 12(03): 258-261.
[4]黄鹤，刘一恒，赵熙，许哲，郭璐.多层多源信息融合旋翼无人机测高算法[J].中国惯性技术学报，2018，26(03)：316-322+329.[4] Huang He, Liu Yiheng, Zhao Xi, Xu Zhe, Guo Lu. Multi-layer multi-source information fusion rotor UAV altimetry algorithm [J]. Chinese Journal of Inertial Technology, 2018, 26(03): 316-322+ 329.
发明内容SUMMARY OF THE INVENTION
针对现有无人机定位方法，考虑的场景较为理想，并且多是对定位方法的性能进行定量的分析，对实际应用场景的考虑有所欠缺的问题，本发明结合实际的应用场景，根据信道衰减模型，提出了一种基于多源测向信息融合的无人机定位方法。For the existing UAV positioning method, the considered scenarios are ideal, and most of them are quantitative analysis of the performance of the positioning method, and the consideration of the actual application scenario is lacking. The present invention combines the actual application scenario, according to the channel Attenuation model, a UAV positioning method based on multi-source direction finding information fusion is proposed.
本发明的一种基于多源测向信息融合的无人机定位方法，包括如下步骤：A UAV positioning method based on multi-source direction finding information fusion of the present invention includes the following steps:
步骤1：部署在不同探测位置的站点获取无人机测向信息，并打包发送给测控中心；Step 1: Sites deployed in different detection positions obtain the direction finding information of the UAV, and package and send it to the measurement and control center;
所述的无人机测向信息包括无人机信号强度、信干噪比和无人机信号的波达方向；The UAV direction finding information includes UAV signal strength, signal-to-interference-noise ratio and the direction of arrival of the UAV signal;
步骤2：测控中心收到n个站点的无人机测向信息，对目标位置进行初步估计；n为大于等于3的整数；测控中心根据任意的站点对，计算获得一个目标无人机的位置；Step 2: The measurement and control center receives the UAV direction finding information of n sites, and makes a preliminary estimate of the target position; n is an integer greater than or equal to 3; the measurement and control center calculates and obtains the position of a target UAV according to any site pair ;
步骤3：测控中心对目标无人机位置进行优化；Step 3: The measurement and control center optimizes the position of the target UAV;
根据站点发送来的信干噪比来对计算的所有目标无人机的位置进行线性加权求和，来获得目标无人机的最终位置，具体是：Perform a linear weighted summation of the calculated positions of all target UAVs according to the signal-to-interference-noise ratio sent by the station to obtain the final position of the target UAV, specifically:
设根据站点i获得的目标无人机的位置为pi＝(xi,yi,zi)，从站点i获得的信干噪比为Γi，i＝1,2,…,n，则测控中心根据n个站点发送来的无人机测向信息，确定最终目标无人机的位置pt＝(xt,yt,zt)为：Suppose the position of the target UAV obtained from site i is pi =(x i ,y i ,z i ), the signal-to-interference-to-noise ratio obtained from site i is Γ i , i=1,2,...,n, Then the measurement and control center determines the position p t = (x t , y t , z t ) of the final target UAV according to the UAV direction finding information sent by n stations as:
所述的测控中心设置有数据处理板，当收到至少3个站点的无人机测向信息时，数据处理板开始对目标无人机位置执行所述的步骤2和3。The measurement and control center is provided with a data processing board. When receiving the direction finding information of the UAVs from at least 3 sites, the data processing board starts to perform the steps 2 and 3 on the position of the target UAV.
本发明与现有技术相比，具有以下明显优势：本发明提供了一种实用、快速的无人机定位方法，考虑到不同站点与无人机的距离不同，针对不同的无人机位置粗估计值，结合对应测向站点的SINR信息，对多个粗估计的位置信息进行有效融合，进而实现对无人机位置的精确估计。本发明方法充分利用已有的测量信息，考虑到不同基站的位置信息以及接收信号的SINR，计算目标无人机位置的过程简单，具有低复杂度，实时性高。Compared with the prior art, the present invention has the following obvious advantages: the present invention provides a practical and fast UAV positioning method. The estimated value, combined with the SINR information of the corresponding direction-finding site, effectively fuses multiple roughly estimated position information, thereby realizing accurate estimation of the UAV position. The method of the invention makes full use of the existing measurement information, and takes into account the position information of different base stations and the SINR of the received signal, the process of calculating the position of the target UAV is simple, with low complexity and high real-time performance.
附图说明Description of drawings
图1为本发明的基于多源测向信息融合的无人机定位方法的整体流程图；Fig. 1 is the overall flow chart of the UAV positioning method based on multi-source direction finding information fusion of the present invention;
图2为本发明的基于多源测向信息融合的无人机定位方法的仿真结果示意图，其中a是传统无人机定位方法的结果，b是本发明方法的定位结果。2 is a schematic diagram of the simulation result of the UAV positioning method based on multi-source direction finding information fusion of the present invention, wherein a is the result of the traditional UAV positioning method, and b is the positioning result of the method of the present invention.
具体实施方式Detailed ways
为了便于本领域普通技术人员理解和实施本发明，下面结合附图和具体实施例对本发明作进一步的详细描述。In order to facilitate those skilled in the art to understand and implement the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.
本发明提供的一种基于多源测向信息融合的无人机定位方法，对多个站点的测向信息进行有效融合，进而实现对防控区域内的无人机的位置进行精确估计。如图1所示，为实现本发明方法的一个流程示例，下面说明各步骤。The invention provides a UAV positioning method based on multi-source direction finding information fusion, which effectively fuses direction finding information of multiple sites, thereby realizing accurate estimation of the position of the UAV in the prevention and control area. As shown in FIG. 1 , in order to realize an example of the flow of the method of the present invention, each step is described below.
步骤1：多站点获取无人机测向信息。Step 1: Multi-site acquisition of UAV direction finding information.
在不同探测位置的站点分布安装有探测系统，用于采集无人机探测信号。Detection systems are installed at sites at different detection locations to collect UAV detection signals.
各站点的探测系统包括无线信号强度传感器、数据储存模块和DOA估计模块等。探测系统通过无线信号强度传感器探测到无人机信号强度，通过无线通信信道模型确定站点的SINR(Signal to Interference plus Noise Ratio，信号与干扰加噪声比)，即信干噪比，并存入数据存储模块，通过DOA估计模块估计所接收的无人机信号的波达方向或波达角。The detection system of each site includes wireless signal strength sensor, data storage module and DOA estimation module. The detection system detects the signal strength of the UAV through the wireless signal strength sensor, and determines the SINR (Signal to Interference plus Noise Ratio, signal to interference plus noise ratio) of the site through the wireless communication channel model, that is, the signal to interference and noise ratio, and stores the data. The storage module estimates the direction of arrival or the angle of arrival of the received UAV signal through the DOA estimation module.
各站点将探测和计算的无人机测向信息发送给测控中心。无人机测向信息，包括无人机信号强度、信干噪比SINR和无人机信号的波达方向打包发送到测控中心。Each station sends the detected and calculated UAV direction finding information to the measurement and control center. The UAV direction finding information, including UAV signal strength, signal-to-interference-noise ratio (SINR) and UAV signal direction of arrival, is packaged and sent to the measurement and control center.
步骤2：测控中心基于多站点发送来的无人机测向信息对目标位置初步估计。Step 2: The measurement and control center preliminarily estimates the target position based on the UAV direction finding information sent from multiple sites.
测控中心位于地面，通过数据接口板获得各站点发送来的网络数据包，对数据包进行接卸，获取无人机信号强度和DOA等无人机测向信息。通过高速数据处理板根据所获得的测向信息，实时计算无人机方位。数据接口板可通过一块可编程门阵列FPGA模块来实现，高速数据处理板可通过一块数字信号处理DSP模块实现。The measurement and control center is located on the ground. It obtains the network data packets sent by each site through the data interface board, unloads the data packets, and obtains the UAV signal strength and DOA and other UAV direction finding information. According to the obtained direction finding information, the UAV azimuth is calculated in real time through the high-speed data processing board. The data interface board can be realized by a programmable gate array FPGA module, and the high-speed data processing board can be realized by a digital signal processing DSP module.
测控中心在接收到至少3个站点对目标无人机的测向信息时，数据处理板对目标位置进行粗略估计。当n≥3时，粗略估计的目标位置可能形成一个估计空间。When the measurement and control center receives the direction finding information of the target UAV from at least 3 sites, the data processing board makes a rough estimation of the target position. When n ≥ 3, the roughly estimated target positions may form an estimation space.
在步骤2中，已知两个测向站点的测向信息，便可实现对无人机位置的估计。In step 2, the position of the UAV can be estimated by knowing the direction finding information of the two direction finding stations.
对于目标无人机，站点1和站点2可以分别测到不同的方位角α(1)和α(2)以及高度角β(1)和β(2)；其中，α(1)∈[-π,π]，α(2)∈[-π,π]，β(1)∈[0,π]，β(2)∈[0,π]；假设站点1的位置坐标为S(1)＝(x(1),y(1),z(1))，x(1),y(1),z(1)分别表示站点1的经度、纬度和高度；站点2位置坐标为S(2)＝(x(2),y(2),z(2))，x(2),y(2),z(2)分别表示站点2的经度、纬度和高度。则无人机的位置坐标p＝(x,y,z)可以由以下公式得到：For the target UAV, station 1 and station 2 can measure different azimuth angles α (1) and α (2) and altitude angles β (1) and β (2) respectively; where α (1) ∈ [- π,π], α (2) ∈[-π,π], β (1) ∈[0,π], β (2) ∈[0,π]; assuming that the location coordinate of station 1 is S (1) =(x (1) , y (1) , z (1) ), x (1) , y (1) , z (1) represent the longitude, latitude and altitude of site 1 respectively; the location coordinates of site 2 are S ( 2) = (x (2) , y (2) , z (2) ), x (2) , y (2) , z (2) represent the longitude, latitude and altitude of site 2, respectively. Then the position coordinate p=(x, y, z) of the UAV can be obtained by the following formula:
根据任意的站点对，可以对目标无人机的位置进行粗略估计，即求解公式(1)的(x,y,z)信息。According to any site pair, the position of the target UAV can be roughly estimated, that is, the (x, y, z) information of formula (1) can be solved.
步骤3：测控中心基于站点先验信息对目标位置进行优化。Step 3: The measurement and control center optimizes the target location based on site prior information.
针对多个站点的测向信息，为了提升对目标的定位估计效率，数据处理板结合步骤2中粗略估计的无人机位置，利用各站点的位置先验信息，对目标位置进行精确估计。For the direction finding information of multiple sites, in order to improve the efficiency of target positioning estimation, the data processing board combines the roughly estimated UAV position in step 2, and uses the position prior information of each site to accurately estimate the target position.
每个粗估计的无人机位置都对应一定的SINR，因此本发明进一步利用各站点接收信号的SINR，考虑到SINR值较大时，对应站点所提供的位置信息较为准确，提出对多个粗估计的位置基于SINR线性加权求和进行数据融合，得到一个精确的无人机位置。设站点a提供的位置为pa＝(xa,ya,za)，SINR为Γa，站点b提供的位置为pb＝(xb,yb,zb)，SINR为Γb，站点c提供的位置为pc＝(xc,yc,zc)，SINR为Γc，则最终目标无人机的位置pt＝(xt,yt,zt)为：Each rough estimated UAV position corresponds to a certain SINR. Therefore, the present invention further utilizes the SINR of the signal received by each station. Considering that when the SINR value is large, the position information provided by the corresponding station is more accurate. The estimated position is fused based on the linear weighted summation of SINR to obtain an accurate UAV position. Suppose the position provided by site a is p a =(x a , y a , za ), the SINR is Γ a , the position provided by site b is p b =(x b , y b , z b ), and the SINR is Γ b , the position provided by site c is p c =(x c , y c , z c ), and the SINR is Γ c , then the final target UAV position p t = (x t , y t , z t ) is:
如图2所示，为本发明基于多源测向信息融合的无人机定位的仿真结果。图2中的a显示的是传统定位方法的仿真结果，图b显示的是本发明方法的定位仿真结果，从两个图中可以看出，本发明方法对无人机的定位更加准确，相对于传统定位方法，定位精度有明显的提升。As shown in FIG. 2 , it is the simulation result of the UAV positioning based on the multi-source direction finding information fusion of the present invention. A in Fig. 2 shows the simulation result of the traditional positioning method, and Fig. b shows the positioning simulation result of the method of the present invention. It can be seen from the two figures that the method of the present invention is more accurate for the positioning of the UAV, and relatively Compared with the traditional positioning method, the positioning accuracy has been significantly improved.
综上，可以看出，本发明利用不同站点所处的外界环境状况，根据各站点测向信息的置信度，对多个站点的测向信息进行有效融合，最终实现对无人机的有效定位。本发明根据重点防护区域的多个测向站点位置以及无人机的初步位置估计信息，提出相应的定位优化方法，对于“黑飞”无人机的管制提供了有效的技术支持，具有重要的理论意义及应用价值。To sum up, it can be seen that the present invention utilizes the external environmental conditions where different sites are located, and according to the confidence of the direction finding information of each site, effectively integrates the direction finding information of multiple sites, and finally realizes the effective positioning of the UAV. . The invention proposes a corresponding positioning optimization method according to the positions of multiple direction finding stations in the key protection area and the preliminary position estimation information of the UAV, provides effective technical support for the control of "black flying" UAV, and has important advantages. Theoretical significance and application value.
Claims (3)
1.一种基于多源测向信息融合的无人机定位方法，其特征在于，包括：1. an unmanned aerial vehicle positioning method based on multi-source direction finding information fusion, is characterized in that, comprises: 步骤1：部署在不同探测位置的站点获取无人机测向信息，并打包发送给测控中心；Step 1: Sites deployed in different detection positions obtain the direction finding information of the UAV, and package and send it to the measurement and control center; 所述的无人机测向信息包括无人机信号强度、信干噪比和无人机信号的波达方向；The UAV direction finding information includes UAV signal strength, signal-to-interference-noise ratio and the direction of arrival of the UAV signal; 步骤2：测控中心收到n个站点的无人机测向信息，对目标无人机位置进行初步计算；n为大于等于3的整数；测控中心根据任意的站点对，计算获得一个目标无人机的位置；Step 2: The measurement and control center receives the UAV direction finding information of n sites, and performs a preliminary calculation on the position of the target UAV; n is an integer greater than or equal to 3; the measurement and control center calculates and obtains a target unmanned aerial vehicle according to any site pair the location of the machine; 步骤3：测控中心对目标无人机位置进行优化；Step 3: The measurement and control center optimizes the position of the target UAV; 根据站点发送来的信干噪比来对计算的所有目标无人机的位置进行线性加权求和，来获得目标无人机的最终位置，具体是：Perform a linear weighted summation of the calculated positions of all target UAVs according to the signal-to-interference-noise ratio sent by the station to obtain the final position of the target UAV, specifically: 设根据站点i获得的目标无人机的位置为pi＝(xi,yi,zi)，从站点i获得的信干噪比为Γi，i＝1,2,…,n，则测控中心根据n个站点发送来的无人机测向信息，确定最终目标无人机的位置pt＝(xt,yt,zt)为：Suppose the position of the target UAV obtained from site i is pi =(x i ,y i ,z i ), the signal-to-interference-to-noise ratio obtained from site i is Γ i , i=1,2,...,n, Then the measurement and control center determines the position p t = (x t , y t , z t ) of the final target UAV according to the UAV direction finding information sent by n stations as: 2.根据权利要求1所述的方法，其特征在于，所述的步骤2中，根据两个站点测量到的方位角和高度角，以及站点位置来初步计算目标无人机的位置。2 . The method according to claim 1 , wherein in the step 2, the position of the target UAV is preliminarily calculated according to the azimuth angle and the altitude angle measured by the two stations and the position of the station. 3 . 3.根据权利要求1或2所述的方法，其特征在于，所述的测控中心设置有数据处理板，当收到至少3个站点的无人机测向信息时，数据处理板开始对目标无人机位置执行所述的步骤2和3。3. method according to claim 1 or 2, is characterized in that, described measurement and control center is provided with data processing board, when receiving the UAV direction finding information of at least 3 sites, data processing board starts to target Perform steps 2 and 3 as described for the drone location.
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