A method and system for locating unmanned aerial vehicles (UAVs)

Abstract

This invention provides a method and system for UAV positioning. The method includes: receiving current UAV signal data transmitted by multiple receiving stations; the multiple receiving stations include a master station and multiple slave stations for collecting UAV signals; calculating the arrival time difference between the current UAV signal arriving at each slave station and arriving at the master station based on the acquisition time in the current UAV signal data transmitted by the multiple receiving stations; and calculating the current position of the UAV by applying the M-estimation algorithm based on the arrival time difference between the current UAV signal arriving at each slave station and arriving at the master station, and the coordinate positions of the multiple receiving stations. This invention reduces the adverse effects of multipath propagation, non-line-of-sight propagation, and occlusion on the positioning results, and improves the accuracy and reliability of UAV positioning.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of unmanned aerial vehicle positioning, and more particularly relates to an unmanned aerial vehicle positioning method and system. BACKGROUND

[0002] With the increasing maturity of unmanned aerial vehicle technology, unmanned aerial vehicles have been applied in more and more military or civilian scenarios. In order to better monitor and control unmanned aerial vehicles, obtaining the position of the unmanned aerial vehicle has become a prerequisite for many applications. Unlike the positioning of conventional civil aviation aircraft, unmanned aerial vehicles usually do not have the ability to autonomously report their own positions to the outside world. Furthermore, the use scenarios of unmanned aerial vehicles are usually urban environments, which are not suitable for using radar and other active detection devices that emit electromagnetic waves.

[0003] Passive positioning has broad application prospects in the fields of wireless communication, target monitoring, electronic countermeasures, navigation and telemetry. Currently, passive positioning methods are usually used to position unmanned aerial vehicles. However, in actual engineering, multipath effects, non-line-of-sight propagation, and occlusion inevitably exist in the propagation of radio waves. The existing passive positioning methods have low fault tolerance to multipath effects, non-line-of-sight propagation, and occlusion, resulting in low positioning accuracy of unmanned aerial vehicles. SUMMARY

[0004] In view of the defects of the prior art, the purpose of the present application is to provide an unmanned aerial vehicle positioning method and system, which aims to solve the problem of low positioning accuracy of the existing unmanned aerial vehicle positioning method.

[0005] To achieve the above-mentioned purpose, in a first aspect, the present application provides an unmanned aerial vehicle positioning method, comprising:

[0006] S101 receiving current unmanned aerial vehicle signal data sent by a plurality of receiving stations; the plurality of receiving stations include a master station and a plurality of slave stations for collecting unmanned aerial vehicle signals;

[0007] S102 calculating the difference between the arrival time of the current unmanned aerial vehicle signal at each slave station and the arrival time at the master station based on the collection time in the current unmanned aerial vehicle signal data sent by the plurality of receiving stations;

[0008] S103 applying an M estimation algorithm based on the difference between the arrival time of the current unmanned aerial vehicle signal at each slave station and the arrival time at the master station, and the coordinate positions of the plurality of receiving stations, to calculate the current position of the unmanned aerial vehicle.

[0009] In an optional example, S103 specifically includes:

[0010] establishing a target function , :

[0011] , = 2, 3,..., M;

[0012] , = 2, 3,..., M;

[0013] ;

[0014] ;

[0015] ;

[0016] ;

[0017] ;

[0018] Solving , , respectively , ;

[0019] According to and , the current position of the unmanned aerial vehicle is calculated :

[0020] ;

[0021] ;

[0022] ;

[0023] ;

[0024] ;

[0025] ;

[0026] wherein M is the total number of receiving stations, wherein the serial number 1 represents the master station, and the serial numbers = 2, 3,..., M represent each slave station, denotes the M estimation function, denotes the electromagnetic wave propagation speed, denotes the current unmanned aerial vehicle electromagnetic wave arrival time at the slave station , denotes the current unmanned aerial vehicle electromagnetic wave arrival time at the master station, denotes the current unmanned aerial vehicle distance to the master station, denotes the coordinate position of the master station, denotes the coordinate position of the slave station .

[0027] In an optional example, the memory , Specifically, it is expressed by the following formula:

[0028] , wherein is a robust threshold coefficient.

[0029] In an optional example, the solution is obtained , including:

[0030] 1) Set the initial value of iteration: , is a preset coefficient, is a unit matrix; ;

[0031] 2) Calculate the residual error , =2, 3, …, M;

[0032] 3) Calculate the adaptive matrix of M estimation , wherein is the derivative of ;

[0033] 4) Calculate the estimation gain matrix ;

[0034] 5) Update the estimator ;

[0035] 6) Update the measurement error covariance matrix: ;

[0036] 7) If <M, set to , and perform the next iteration calculation according to steps 2) to 6); if =M, stop the iteration calculation, and obtain .

[0037] In a second aspect, the present application provides a UAV positioning system, comprising:

[0038] a signal receiving module, configured to receive current UAV signal data sent by a plurality of receiving stations; the plurality of receiving stations include one master station and a plurality of slave stations for collecting UAV signals;

[0039] a time difference calculation module, configured to calculate the difference between the arrival time of the current UAV signal at each slave station and the arrival time at the master station based on the collection time in the current UAV signal data sent by the plurality of receiving stations;

[0040] The UAV positioning module is configured to calculate the current position of the UAV based on the difference between the time of arrival of the current UAV signal at each slave station and the time of arrival at the master station, and the coordinate positions of the plurality of receiving stations, and apply an M-estimation algorithm.

[0041] In an optional example, the UAV positioning module specifically comprises:

[0042] A target function construction unit is configured to establish a target function 、 :

[0043] , =2, 3, …, M;

[0044] , =2, 3, …, M;

[0045] ;

[0046] ;

[0047] ;

[0048] ;

[0049] ;

[0050] A target function solving unit is configured to solve 、 , respectively, to obtain 、 ;

[0051] A UAV positioning unit is configured to calculate the current position of the UAV and : :

[0052] ;

[0053] ;

[0054] ;

[0055] ;

[0056] ;

[0057] ;

[0058] wherein M is the total number of receiving stations, wherein the serial number 1 represents the master station, and the serial numbers 2, 3, …, M represent the slave stations. = 2, 3,..., M, respectively represent each slave station, represents an M estimation function, represents an electromagnetic wave propagation speed, represents a current unmanned aerial vehicle electromagnetic wave arrival time of a slave station , represents a current unmanned aerial vehicle electromagnetic wave arrival time of a master station, represents a current unmanned aerial vehicle distance to a master station, represents a coordinate position of a master station, represents a coordinate position of a slave station .

[0059] In an optional example, the objective function construction unit is specifically recorded as , which is specifically expressed by the following formula:

[0060] wherein is a robust threshold coefficient.

[0061] In an optional example, the objective function solving unit is specifically used for:

[0062] 1) setting an iteration initial value: , is a preset coefficient, is a unit matrix; ;

[0063] 2) calculating a residual error , = 2, 3,..., M;

[0064] 3) calculating an adaptive matrix of M estimation , wherein is a derivative of ;

[0065] 4) calculating an estimation gain matrix ;

[0066] 5) updating the estimation ;

[0067] 6) updating the measurement error covariance matrix: ;

[0068] 7) if <M, setting to , and performing next iteration calculation according to steps 2) to 6); if =M, stopping iteration calculation, and obtaining .

[0069] In a third aspect, the present application provides a UAV positioning device, comprising a memory and a processor; the memory is used for storing a computer program; the processor is used for implementing the UAV positioning method according to the first aspect when executing the computer program.

[0070] In a fourth aspect, the present application provides a computer readable storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the UAV positioning method according to the first aspect is implemented.

[0071] Overall, compared with the prior art, the above technical solutions conceived by the present application have the following beneficial effects:

[0072] The present application provides a UAV positioning method and system, which calculates the difference between the arrival time of the current UAV signal at each slave station and the arrival time at the master station according to the acquisition time in the current UAV signal data sent by multiple receiving stations, and on this basis, combines the coordinate positions of the multiple receiving stations, applies an improved TDOA algorithm based on M estimation to calculate the current position of the UAV, so as to reduce the adverse effects of multipath effect, non-line-of-sight propagation and shielding on the positioning result, and improve the accuracy and reliability of UAV positioning. BRIEF DESCRIPTION OF DRAWINGS

[0073] Figure 1 is one of the flowcharts of the UAV positioning method provided by the present application;

[0074] Figure 2 is the second flowchart of the UAV positioning method provided by the present application;

[0075] Figure 3 is the architecture diagram of the UAV positioning system provided by the present application. DETAILED DESCRIPTION

[0076] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0077] Passive positioning observations cover multiple domain parameters such as space, time, frequency and energy, and the time difference of arrival (TDOA) is one of the most frequently used observations. With the continuous development of modern communication technology and TDOA measurement technology, TDOA positioning technology based on multiple sensors has become one of the most mainstream radiation source positioning methods. This type of positioning system is suitable for wideband signals and can achieve high positioning accuracy.

[0078] To address the need for passive positioning of drones in urban environments, this invention discloses a drone positioning method based on improved TDOA technology. This method can be used to calculate the drone's position. The following is a detailed explanation in conjunction with the appendix. Figure 1 The present invention will be further described in conjunction with the technical solutions and specific embodiments thereof.

[0079] Figure 1 This is one of the flowcharts illustrating the UAV positioning method provided by the present invention, such as... Figure 1 As shown, the execution entity of this method is the data processing center, and the method includes:

[0080] Step S101: Receive current UAV signal data sent by multiple receiving stations; the multiple receiving stations include a master station and multiple slave stations for collecting UAV signals;

[0081] Step S102: Based on the acquisition time in the current UAV signal data sent by multiple receiving stations, calculate the difference in arrival time between the current UAV signal arriving at each slave station and arriving at the master station.

[0082] Step S103: Based on the difference in arrival time between the current UAV signal and the arrival time at each slave station and the master station, and the coordinate positions of multiple receiving stations, the M-estimation algorithm is applied to calculate the current position of the UAV.

[0083] Figure 2 This is the second flowchart illustrating the UAV positioning method provided by the present invention, as shown below. Figure 2 As shown, the specific process of this method is as follows: First, establish a model for the positioning method, assuming an unknown UAV and M (M≥4) signal receiving stations with known locations, which are used to collect UAV signals; Second, select one of the receiving stations as the master station and the remaining M-1 as slave stations, and determine the coordinate positions of each receiving station, which can be spatial positions in a Cartesian coordinate system based on the Earth; Third, each receiving station collects UAV signal data in real time; Fourth, each receiving station sends the UAV signal data collected at the current moment, i.e., the current UAV signal data, to the data processing center for the data processing center to calculate the time difference of arrival; Fifth, the data processing center uses the improved TDOA algorithm to calculate the current position of the UAV and outputs it; Sixth, return to step three and calculate the UAV position based on the data received by the receiving station at the next moment.

[0084] After receiving the current drone signal data, the data processing center can first calculate the difference in arrival time between the drone signal reaching each slave station and reaching the master station, based on the acquisition time in the current drone signal data: ,in This indicates that the electromagnetic waves emitted by the drone have reached the slave station. The arrival time, i.e., from the station The acquisition time in the current drone signal data, This indicates the arrival time of the drone's electromagnetic waves to the main station, which is the acquisition time in the current drone signal data of the main station. =2, 3, ..., M; then, based on the difference in arrival time between the current UAV signal arriving at each slave station and arriving at the master station, and the coordinate positions of multiple receiving stations, the improved TDOA algorithm is applied to calculate the current position of the UAV.

[0085] It should be noted that, considering the inevitable multipath effects, non-line-of-sight propagation, and occlusion in the detection results of receiving stations in actual environments, existing TDOA is generally susceptible to the influence of multipath effects, non-line-of-sight propagation, and occlusion. The embodiments of this invention introduce robust M-estimation into the UAV positioning method, which improves the traditional TDOA algorithm. It can be used to reduce the adverse effects of multipath effects, non-line-of-sight propagation, and occlusion on TDOA technology, thereby reducing the adverse effects of abnormal data on the results, and thus improving the accuracy and reliability of UAV positioning, which has certain engineering application value.

[0086] The method provided in this invention calculates the difference between the arrival time of the current UAV signal at each slave station and the arrival time at the master station based on the acquisition time in the current UAV signal data sent by multiple receiving stations. On this basis, combined with the coordinate positions of multiple receiving stations, an improved TDOA algorithm based on M estimation is applied to calculate the current position of the UAV. This can reduce the adverse effects of multipath effect, non-line-of-sight propagation, and occlusion on the positioning results, and improve the accuracy and reliability of UAV positioning.

[0087] Based on the above embodiments, step S103 specifically includes:

[0088] Establish the objective function , :

[0089] , =2, 3, ..., M;

[0090] , =2, 3, ..., M;

[0091] ;

[0092] ;

[0093] ;

[0094] ;

[0095] ;

[0096] an objective function solving unit for solving , , respectively, to obtain , ;

[0097] a UAV positioning unit for calculating the current position of the UAV according to ;

[0098] ;

[0099] ;

[0100] ;

[0101] ;

[0102] ;

[0103] ;

[0104] wherein M is the total number of receiving stations, wherein the serial number 1 represents the master station, and the serial numbers =2, 3, …, M represent the respective slave stations, denotes an M estimation function, denotes the electromagnetic wave propagation speed, denotes the current UAV electromagnetic wave arrival time at the slave station , denotes the current UAV electromagnetic wave arrival time at the master station, denotes the current UAV distance to the master station, denotes the coordinate position of the master station, denotes the coordinate position of the slave station .

[0105] Based on any of the above embodiments, let , be specifically expressed by the following formula:

[0106] wherein is a robust threshold coefficient.

[0107] Based on any of the above embodiments, solve to obtain , including:

[0108] 1) Set the initial value of iteration: , is a preset coefficient,​ is the identity matrix; ;

[0109] 2) Calculate the residual , = 2, 3,..., M;

[0110] 3) Calculate the adaptive matrix of M - estimation , where is the derivative of;

[0111] 4) Calculate the estimated gain matrix ;

[0112] 5) Update the estimator ;

[0113] 6) Update the measurement error covariance matrix: ;

[0114] 7) If < M, then set to , and perform the next iteration calculation according to steps 2) to 6); if = M, then stop the iteration calculation and obtain .

[0115] It should be noted that will be adaptively adjusted according to the residual . Whether the residual is positively large or negatively large, will decrease as the absolute value of the residual increases, thereby reducing the weight of the abnormal data and reducing the adverse impact of the abnormal data on ; Therefore, using the robustness of M - estimation, a more accurate can be obtained.

[0116] Furthermore, a similar method as above can be used to solve to obtain . Then, according to and , calculate , and finally the current position of the UAV can be obtained.

[0117] Based on any of the above embodiments, the present invention provides a UAV positioning method based on an improved TDOA technology. The basic positioning model consists of M receiving stations. Assuming that M receiving stations are used to locate a target radiation source in a two - dimensional plane, the positions of each receiving station are , ([[]]END]] =1, 2, 3, ..., M), the target's location is Furthermore, it is assumed that the electromagnetic wave propagates along the line of sight between each receiving station and the target, neglecting the case of non-line-of-sight propagation. Let the electromagnetic wave travel from the target to the [missing information - likely a specific location or stage]. The propagation time of each receiving station is The first receiving station is designated as the master receiving station, and the remaining receiving stations are designated as slave receiving stations, based on the positioning model. The TDOA measurement values ​​of the first receiving station and the first receiving station are , ( =2, 3, ..., M).

[0118] Represent TDOA measurements in vector form The covariance matrix is ​​Q.

[0119] When measurement error exists, the following formula applies:

[0120] , ( =2,3,...,M)

[0121] in, Indicates the first The actual TDOA values ​​of the first receiving station and the first receiving station. This indicates the corresponding measurement error. When multipath effects, non-line-of-sight propagation, or obstruction occur, electromagnetic waves no longer propagate in a straight line; reflection, refraction, or even failure to propagate may occur. It will be bigger.

[0122] Large measurement error This will cause TDOA measurement values Compared with the true value of TDOA There are significant differences between the measured values. Inaccurate calculations can affect the determination of the target's location, i.e., the drone's position.

[0123] Let the measurement error vector .

[0124] Target and the The distance between the receiving stations is

[0125] Then the target reaches the first The distance difference between each receiving station and the main receiving station is

[0126]

[0127] time difference Recorded as Distance difference This can be expressed as the propagation speed of electromagnetic waves. cThe product of the time difference .

[0128]

[0129] Solving this equation can get the XYZ position coordinates of the UAV.

[0130] The above equation can be arranged as

[0131] , =2,3, …, M

[0132] Wherein

[0133]

[0134] The above equation can be written as a matrix form

[0135]

[0136]

[0137]

[0138]

[0139] In the above equation is the distance from the target of the UAV to the No. 1 main receiving station, which is an unknown quantity and cannot be directly calculated, and needs to be transformed as follows:

[0140]

[0141] Then The solution of can be expressed as The solution of and the solution of times, that is:

[0142]

[0143] Use scalar to express:

[0144]

[0145] And: , substitute the above equation, then:

[0146]

[0147]

[0148] Let: , , Then the above formula is recorded as:

[0149] The solution can be obtained The calculation method provided by the application can further improve the positioning accuracy of the unmanned aerial vehicle.

[0150] For the solution of and , the least square method can be used for calculation.

[0151] The recursive least square method is used to calculate and , and the objective functions are respectively established:

[0152] , =2, 3,..., M

[0153] , =2, 3,..., M

[0154] and The calculation method is the same, and the calculation method provided by the application will be described in detail below taking as an example.

[0155] The unmanned aerial vehicle position calculated by the above method is easy to be affected by measurement error, the application introduces M estimation with robustness, improves the traditional TDOA algorithm, and uses the improved algorithm to calculate , , and then calculates the unmanned aerial vehicle position based on this.

[0156] The objective function is recorded as:

[0157]

[0158] Record , then:

[0159]

[0160] Wherein represents the M estimation function, which can be specifically expressed by the following formula.

[0161] . Wherein is a robust threshold coefficient;

[0162] The improved TDOA method is used to calculate the objective function according to the following steps, and is obtained:

[0163] ​Step 1, set the initial iteration value: , is a preset coefficient, is the identity matrix; .

[0164] Step 2, calculate the residual , = 2, 3,..., M.

[0165] Step 3, calculate the adaptive matrix of M - estimation , where is the derivative of.

[0166] Step 4, calculate the estimated gain matrix

[0167] Step 5, update the estimated quantity .

[0168] Step 6, update the measurement error covariance matrix:

[0169] Step 7, if < M, then set to , and perform the next iteration calculation according to Steps 2 to 6; if = M, then stop the iteration calculation and obtain .

[0170] Then, referring to the above steps, calculate the objective function , and obtain ;

[0171] According to and , calculate the distance from the UAV to the master station.

[0172] Finally, calculate the position of the UAV.

[0173] The UAV positioning method provided by the present invention can reduce the influence of errors, improve the positioning accuracy of the UAV, and the present invention has been applied in the UAV prevention and control project, having certain engineering application value.

[0174] Based on any of the above embodiments, Figure 3 is the architecture diagram of the UAV positioning system provided by the present invention, as shown in Figure 3 , this system is applied to the data processing center, and this system includes:

[0175] The signal receiving module 310 is configured to receive current unmanned aerial vehicle signal data sent by a plurality of receiving stations, wherein the plurality of receiving stations include one master station and a plurality of slave stations for collecting unmanned aerial vehicle signals.

[0176] The time difference calculation module 320 is configured to calculate a time difference between arrival times of the current unmanned aerial vehicle signal at each slave station and at the master station based on collection times in the current unmanned aerial vehicle signal data sent by the plurality of receiving stations.

[0177] The unmanned aerial vehicle positioning module 330 is configured to calculate a current position of the unmanned aerial vehicle by applying an M estimation algorithm based on the time difference between the arrival times of the current unmanned aerial vehicle signal at each slave station and at the master station and coordinate positions of the plurality of receiving stations.

[0178] The system provided by the embodiment of the present application can calculate a time difference between arrival times of current unmanned aerial vehicle signals at each slave station and at the master station based on collection times in current unmanned aerial vehicle signal data sent by a plurality of receiving stations, and can calculate a current position of the unmanned aerial vehicle by applying an improved TDOA algorithm based on M estimation in combination with coordinate positions of the plurality of receiving stations, thereby being capable of reducing adverse effects of multipath effects, non-line-of-sight propagation and shielding on positioning results and improving precision and reliability of unmanned aerial vehicle positioning.

[0179] It can be understood that detailed function implementations of the above modules can be referred to the foregoing method embodiments, which will not be described herein.

[0180] In addition, the embodiment of the present application provides another unmanned aerial vehicle positioning device, which comprises a memory and a processor.

[0181] The memory is configured to store a computer program.

[0182] The processor is configured to implement the method in the above embodiment when executing the computer program.

[0183] In addition, the present application also provides a computer readable storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the method in the above embodiment is implemented.

[0184] Based on the method in the above embodiment, the embodiment of the present application provides a computer program product, which makes the processor execute the method in the above embodiment when the computer program product is run on the processor.

[0185] Those skilled in the art can easily understand that the above description is only the preferred embodiment of the present application, and is not used to limit the present application, and any modification, equivalent replacement and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for positioning a drone, the method comprising: The method comprises: S101 receiving current unmanned aerial vehicle signal data sent by a plurality of receiving stations; The plurality of receiving stations comprise one master station and a plurality of slave stations for collecting unmanned aerial vehicle signals; S102 calculating the difference between the arrival time of the current unmanned aerial vehicle signal at each slave station and the arrival time at the master station based on the collection time in the current unmanned aerial vehicle signal data sent by the plurality of receiving stations; S103 calculating the current position of the unmanned aerial vehicle based on the difference between the arrival time of the current unmanned aerial vehicle signal at each slave station and the arrival time at the master station, and the coordinate positions of the plurality of receiving stations, and applying an M estimation algorithm; S103 specifically comprises: Establishing an objective function , : ， =2，3，…，M； ， =2，3，…，M； ； ； ； ； ； solving , , respectively, to obtain , ; According to and , the current position of the UAV is calculated : ； ； ； ； ； ； Where M represents the total number of receiving stations, and serial number 1 represents the master station, and serial number... =2, 3, ..., M represent the various slave stations. Denotes the M estimation function, Indicates the speed of electromagnetic wave propagation. This indicates that the electromagnetic waves from the drone have reached the slave station. Arrival time, This indicates the arrival time of the drone's electromagnetic waves to the main station. This indicates the current distance of the drone from the main station. Indicates the coordinates of the main station. Indicates from station The coordinates of the location; Note , In particular, this is expressed by the following equation: wherein is a robust threshold coefficient. 2.The UAV positioning method of claim 1, wherein, solving , obtaining , comprising: 1) Set iteration initial value: , is a preset coefficient, is an identity matrix; ; 2) Compute residual , = 2, 3,..., M; 3) Compute the adaptive matrix of M estimates where is the derivative of ; 4) Compute estimated gain matrix ; 5) updating the estimator ; 6) Update the measurement error covariance matrix: ; 7) if < M, then set < M, then set , and perform the next iteration calculation according to steps 2) to 6); if = M, then stop the iteration calculation and obtain .

3. A drone positioning system, characterized in that, The method comprises: A signal receiving module for receiving current unmanned aerial vehicle signal data sent by a plurality of receiving stations; The plurality of receiving stations comprise one master station and a plurality of slave stations for collecting unmanned aerial vehicle signals; A time difference calculation module for calculating the difference between the arrival time of the current unmanned aerial vehicle signal at each slave station and the arrival time at the master station based on the collection time in the current unmanned aerial vehicle signal data sent by the plurality of receiving stations; An unmanned aerial vehicle positioning module for calculating the current position of the unmanned aerial vehicle based on the difference between the arrival time of the current unmanned aerial vehicle signal at each slave station and the arrival time at the master station, and the coordinate positions of the plurality of receiving stations, and applying an M estimation algorithm; The unmanned aerial vehicle positioning module specifically comprises: a target function construction unit, configured to establish a target function 、 : ， =2，3，…，M； ， =2，3，…，M； ； ； ； ； ； a target function solving unit for solving , , respectively, to obtain , ; a drone positioning unit for calculating a current position of the drone based on and :​ ； ； ； ； ； ； Where M represents the total number of receiving stations, and serial number 1 represents the master station, and serial number... =2, 3, ..., M represent the various slave stations. Denotes the M estimation function, Indicates the speed of electromagnetic wave propagation. This indicates that the electromagnetic waves from the drone have reached the slave station. Arrival time, This indicates the arrival time of the drone's electromagnetic waves to the main station. This indicates the current distance of the drone from the main station. Indicates the coordinates of the main station. Indicates from station The coordinates of the location; The target function construction unit records , Specifically, the following formula is used: wherein is a robust threshold coefficient.

4. The drone positioning system of claim 3, wherein, The objective function solving unit specifically functions to: 1) Set iteration initial value: , is a preset coefficient, is an identity matrix; ; 2) Compute residual , = 2, 3,..., M; 3) Compute the adaptive matrix of M estimates where is the derivative of ; 4) Compute estimated gain matrix ; 5) updating the estimator ; 6) Update the measurement error covariance matrix: ; 7) if < M, then set < M, then set , and perform the next iteration according to steps 2) to 6); if = M, then stop the iteration and obtain .

5. A drone positioning apparatus, comprising: A memory and a processor; the memory is used to store a computer program; the processor is used to realize the unmanned aerial vehicle positioning method as claimed in any one of claims 1-2 when executing the computer program.

6. A computer-readable storage medium, characterized in that, The storage medium has a computer program stored thereon, and the computer program realizes the unmanned aerial vehicle positioning method as claimed in any one of claims 1-2 when executed by the processor.

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