Unmanned aerial vehicle positioning method, device and equipment and storage medium

Abstract

The invention discloses an unmanned aerial vehicle positioning method, device and equipment and a storage medium, and the method comprises the steps: obtaining a sound wave signal of an unmanned aerial vehicle, and carrying out the preprocessing of the sound wave signal; wherein the sound wave signal is collected by a microphone array; estimating the position of the unmanned aerial vehicle on the preprocessed sound wave signal based on a preset time delay estimation algorithm to obtain a plurality of candidate positions; wherein the position is represented by a pitch angle and an azimuth angle of the unmanned aerial vehicle; determining a controllable response power-phase conversion SRP-PHAT fitness function based on the preprocessed sound wave signal; wherein the SRP-PHAT fitness function represents a function relationship between the position and the power of the unmanned aerial vehicle; performing optimization iteration on the SRP-PHAT fitness function by adopting a preset optimization algorithm based on the candidate position to obtain a target unmanned aerial vehicle position; the accuracy and robustness of sound source localization are improved, and the calculation complexity is reduced at the same time.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle detection, and in particular to a method, device, equipment and storage medium for positioning an unmanned aerial vehicle. Background Art

[0002] At present, drone detection technology mainly includes radar detection, radio signal detection, acoustic signal detection, photoelectric detection and the comprehensive application of these means. As a passive detection method, acoustic signal detection has high concealment and applicability. It can detect under different meteorological conditions and when the target is obscured, and the deployment cost of its sensor is relatively low, which is particularly suitable for the detection mission of civil drones. Most mainstream civil drones are small rotor-type designs. During the flight, due to the high-speed rotation of the propeller cutting the airflow and the rotation of the motor, it will generate noise with certain characteristics. Therefore, through the collection, processing and analysis of these acoustic signals by acoustic signal detection, the positioning of the drone body can be effectively achieved.

[0003] In the prior art, sound source localization technology is mainly divided into three types: methods based on Time Difference of Arrival (TDOA), methods based on high-resolution spectrum estimation, and methods based on beamforming technology. However, TDOA has poor robustness in complex environments and is easily affected by environmental noise and multipath effects, resulting in large positioning errors. The high-resolution spectrum estimation method has high requirements on factors such as data sampling quality, number of array elements, signal stability, and environmental noise; moreover, the method has a large amount of calculation, and improper parameter configuration will directly affect the accuracy of the positioning results. The computational complexity of beamforming technology is high, which limits its popularity in real-time applications and may affect its response speed. It can be seen that traditional sound source localization methods have problems such as inaccurate positioning and large amount of calculation. Summary of the invention

[0004] The present invention provides a method, device, equipment and storage medium for positioning a drone, so as to solve the problems of inaccurate positioning of drone sound sources and large amount of calculation in the prior art.

[0005] In a first aspect, an embodiment of the present invention provides a method for positioning a drone, comprising:

[0006] Acquire a drone sound wave signal and pre-process the sound wave signal; wherein the sound wave signal is collected by a microphone array;

[0007] The position of the UAV is estimated based on the pre-processed acoustic wave signal based on a preset delay estimation algorithm to obtain a plurality of candidate positions; wherein the position is represented by the pitch angle and azimuth angle of the UAV;

[0008] Determine a controllable response power-phase conversion SRP-PHAT fitness function based on the preprocessed acoustic wave signal; wherein the SRP-PHAT fitness function represents a functional relationship between the position and power of the drone;

[0009] Based on the candidate position, a preset optimization algorithm is used to iterate the SRP-PHAT fitness function to obtain the target UAV position.

[0010] Optionally, preprocessing the sound wave signal includes:

[0011] Calculating a cross power spectrum between two sound wave signals based on generalized cross correlation GCC, wherein the two sound wave signals are a sound wave signal pair, and the sound wave signals collected by the microphone array include a plurality of sound wave signal pairs;

[0012] Based on the phase conversion method PHAT, multiple cross power spectra are weighted and processed to obtain the preprocessed sound wave signal.

[0013] Optionally, the drone position is estimated based on the preprocessed acoustic wave signal based on a preset delay estimation algorithm to obtain multiple candidate positions, including:

[0014] Determine the candidate position range according to the flight data of the UAV and the pre-processed UAV acoustic wave signal, wherein the flight data includes: the flight altitude and flight speed of the UAV;

[0015] A plurality of candidate positions are generated within the candidate position range based on a preset delay estimation algorithm.

[0016] Optionally, the preset optimization algorithm is an adaptive particle swarm optimization algorithm.

[0017] Optionally, optimizing the SRP-PHAT fitness function by using a preset optimization algorithm based on the candidate position to iterate and obtain the target UAV position includes:

[0018] Based on the SRP-PHAT fitness function, a preset number of target candidate positions are selected from multiple candidate positions as the initial particle swarm;

[0019] Based on the initial particle swarm, a preset optimization algorithm is used to iterate the SRP-PHAT fitness function to obtain the target UAV position.

[0020] Optionally, a preset number of target candidate positions are selected from multiple candidate positions based on the SRP-PHAT fitness function as an initial particle swarm, including:

[0021] Based on the SRP-PHAT fitness function, determine the fitness value corresponding to each candidate position; wherein the fitness value represents power information;

[0022] A preset number of target candidate positions are selected from a plurality of candidate positions in a descending order of the fitness values ​​as an initial particle group.

[0023] Optionally, based on the initial particle swarm, a preset optimization algorithm is used to iteratively optimize the SRP-PHAT fitness function to obtain the target UAV position, including:

[0024] Based on the fitness value of each particle in the initial particle swarm and the preset adaptive particle swarm optimization algorithm parameters, the SRP-PHAT fitness function is optimized and iterated according to the adaptive particle swarm optimization algorithm to obtain the target UAV position;

[0025] The adaptive particle swarm optimization algorithm parameters include: initial inertia weight, termination inertia weight, learning factor and maximum number of iterations.

[0026] In a second aspect, an embodiment of the present invention provides a drone positioning device, comprising:

[0027] An acquisition module, used to acquire a drone sound wave signal and pre-process the sound wave signal; wherein the sound wave signal is collected by a microphone array;

[0028] A first determination module is used to estimate the position of the UAV based on the pre-processed acoustic wave signal based on a preset delay estimation algorithm to obtain a plurality of candidate positions; wherein the position is represented by the pitch angle and azimuth angle of the UAV;

[0029] A second determination module is used to determine a controllable response power-phase conversion SRP-PHAT fitness function based on the preprocessed acoustic wave signal; wherein the SRP-PHAT fitness function represents a functional relationship between the position and power of the drone;

[0030] The optimization module is used to optimize and iterate the SRP-PHAT fitness function based on the candidate position using a preset optimization algorithm to obtain the target UAV position.

[0031] In a third aspect, an embodiment of the present invention provides an electronic device, the electronic device comprising:

[0032] at least one processor; and

[0033] a memory communicatively connected to the at least one processor; wherein,

[0034] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the drone positioning method described in any embodiment of the present invention.

[0035] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the drone positioning method described in any embodiment of the present invention when executed.

[0036] The technical solution of the embodiment of the present invention obtains a UAV sound wave signal and pre-processes the sound wave signal; wherein the sound wave signal is collected by a microphone array; the position of the UAV is estimated based on the pre-processed sound wave signal based on a preset delay estimation algorithm to obtain multiple candidate positions; wherein the position is represented by the pitch angle and azimuth angle of the UAV; a controllable response power-phase conversion SRP-PHAT fitness function is determined based on the pre-processed sound wave signal; wherein the SRP-PHAT fitness function represents the functional relationship between the position and power of the UAV; based on the candidate position, the SRP-PHAT fitness function is optimized and iterated using a preset optimization algorithm to obtain the target UAV position, thereby solving the problems of inaccurate UAV sound source positioning and large amount of calculation in the prior art, improving the accuracy and robustness of sound source positioning, and reducing the calculation complexity.

[0037] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0039] Figure 1 A flowchart of a method for positioning a drone provided in Embodiment 1 of the present invention;

[0040] Figure 2 A flowchart of a method for positioning a drone provided in Embodiment 2 of the present invention;

[0041] Figure 3 A complete flow chart of a method for positioning a drone provided in Embodiment 2 of the present invention;

[0042] Figure 4 A schematic diagram of the structure of a UAV positioning device provided in Embodiment 3 of the present invention;

[0043] Figure 5A schematic diagram of the structure of an electronic device that can be used to implement an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0044] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0045] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0046] Embodiment 1

[0047] Figure 1 This is a flow chart of a method for positioning a drone provided in the first embodiment of the present invention. This embodiment is applicable to the situation of positioning a drone. The method can be executed by a drone positioning device. The drone positioning device can be implemented in the form of hardware and / or software. The drone positioning device can be configured in an electronic device. Figure 1 As shown, the method includes:

[0048] S110, obtaining a drone sound wave signal, and preprocessing the sound wave signal; wherein the sound wave signal is collected by a microphone array.

[0049] Among them, the drone sound wave signal can refer to the acoustic pulse signal emitted by the drone. The microphone array can refer to a system composed of multiple microphones, which is used to sample and process the spatial characteristics of the sound field; for example, it can be assumed that the number of array elements on the axis of a microphone array is M_x, and the number of array elements on the axis is M_y, that is, the microphone array can be composed of M_x×M_y=M microphone array elements; in applications of different scenarios, the layout of the microphone array can be adjusted according to the size of the drone and the mission requirements. Preprocessing can refer to denoising, filtering, and feature extraction, which can ensure the quality and reliability of the input data and provide a good foundation for subsequent algorithm processing.

[0050] For example, when the drone is located far away, for example, under the condition of a far-field sound source, the drone sound wave signal can be propagated in the form of a plane wave in free space, and the acoustic pulse signal emitted by the drone can be captured by a microphone array; and the acoustic pulse signal can be pre-processed by denoising, filtering, and feature extraction.

[0051] In this embodiment, the microphone array pre-set according to actual conditions can collect and obtain the acoustic pulse signal, and perform pre-processing such as denoising, filtering and feature extraction on the obtained acoustic pulse signal.

[0052] S120. Estimate the position of the UAV based on the pre-processed acoustic wave signal based on a preset delay estimation algorithm to obtain multiple candidate positions; wherein the positions are represented by the pitch angle and azimuth angle of the UAV.

[0053] Among them, the preset delay estimation algorithm may refer to the TDOA (Time Difference of Arrival) algorithm, which can determine the position of the signal source by measuring the time difference between the signals arriving at different receiving stations. The estimation may refer to the DOA (Direction of Arrival) estimation, which can determine the azimuth or arrival direction of the signal source based on the received signal. Multiple candidate positions may refer to multiple candidate UAV positions; the position may be represented by the pitch angle (Pitch) and azimuth angle of the UAV, which together determine the flight direction and attitude of the UAV. Specifically, the pitch angle of the UAV may refer to the angle between the UAV body axis (along the direction of the nose) and the horizontal plane; the azimuth angle of the UAV, usually refers to the yaw angle (Yaw), which is the angle between the actual heading and the planned heading.

[0054] Specifically, the DOA estimation of the UAV position based on the preprocessed sound wave signal based on the TDOA method can mean that due to the different distances between the sound source and the sensor, the time for the sound to reach different sensors will also be different. This can be achieved by synchronously recording the sound wave signal received by each microphone and then calculating the time difference; based on the calculated time difference, a hyperbola can be constructed in two-dimensional or three-dimensional space; and multiple candidate UAV positions can be obtained by solving the intersection of the hyperbolas.

[0055] In this embodiment, the TDOA method can be used to measure the time difference between the preprocessed sound wave signals reaching different microphones, and a hyperbola can be constructed based on the measured time difference. By solving the intersection of the hyperbola, the pitch angles and azimuth angles of multiple candidate drones can be obtained, and multiple candidate drone positions can be obtained.

[0056] S130. Determine a controllable response power-phase conversion SRP-PHAT fitness function based on the preprocessed acoustic wave signal; wherein the SRP-PHAT fitness function represents a functional relationship between the position and power of the drone.

[0057] Specifically, the Steered Response Power (SRP) comes from the array beamforming theory. In the beamformer, in order to enhance the signal, it is necessary to control it to point to the sound source position. The SRP method estimates the position of the drone sound source by scanning the possible sound source positions in the space and finding the spatial point that maximizes the output power of the steerable beam. The specific steps are described as follows: For a certain time frame n, SRP is a three-dimensional space vector obtained by controlling the delay-sum beamformer. The real-valued function of or P n (X), namely power information; where X is the coordinate of a point in space, P n (X) Always at the drone sound source location (i.e. the position of the drone) obtains the maximum value, so The estimate is:

[0058]

[0059] For example, it can be assumed that a microphone array consists of M microphone elements, where the output of microphone i is m i (t). For a frame of finite length, SRP is defined as:

[0060]

[0061] Among them, ω i is the weight value, T is a time period, t is time, and τ(X,i) is the direct propagation time of the sound wave signal from position X to microphone i, which is defined as:

[0062]

[0063] Where c is the propagation speed of the drone sound wave signal, and X is the position coordinate of microphone i in space. It is known in the prior art that the SRP value can be obtained by accumulating the generalized cross correlation (GCC) function values ​​of all microphone pairs in the microphone array. The GCC value between a pair of microphones k and l can be calculated by the following formula:

[0064]

[0065] Among them, τ is the time difference between the sound wave signal reaching microphones k and l, that is, the delay of the sound wave signal collected by the microphone, * is the conjugate operator, M l (ω), M k (ω) are the acoustic wave signals m l (t), m k The Fourier transform of (t), Ψ kl (ω) is the joint enhancement function in the frequency domain. Converting formula (2) to the frequency domain yields:

[0066]

[0067] Among them, the integrand is the cross power spectrum of the signal between microphones k and l. Since the GCC of all microphone pairs forms a symmetric matrix and the energy of the elements on the diagonal is a fixed value, P can be defined as n The part P′ in (X) that changes with X n (X) is:

[0068]

[0069] Where τ(X,l) is the direct propagation time of the sound wave signal from position X to microphone l; τ(X,k) is the direct propagation time of the sound wave signal from position X to microphone k.

[0070] Furthermore, in order to avoid interference from background noise and other factors on drone positioning, the phase transform method (PHAT) can be used to weight the microphone cross correlation (GCC); the spectrum is whitened and filtered by PHAT to make the peak of GCC sharper, and accurate delay estimation is achieved for broadband signals and quasi-periodic or periodic signals. Therefore, even in noisy and reverberant environments, good performance can be maintained by using the phase transform method PHAT, which is defined as the inverse of the spectrum amplitude:

[0071]

[0072] For a one-dimensional uniform linear array, the TDOA between two microphones l and k can be defined as:

[0073]

[0074] in, is the vector between the Cartesian coordinates of microphones l, k and the origin, is the unit vector of the drone sound source direction vector, θ is the pitch angle of the drone sound source, is the azimuth of the drone sound source. According to formula (8), we can get the function τ(Θ), where: In addition, the DOA of the far-field UAV position can be estimated based on the preprocessed acoustic signal based on SRP-PHAT, which is equivalent to Searching for the incoming wave direction in space can quickly process the drone sound wave signal when the drone is far away, improving the real-time positioning. Among them, the SRP-PHAT method can refer to the method of calculating SRP using PHAT-weighted GCC. Therefore, the controllable response power-phase conversion SRP-PHAT fitness function formula can be:

[0075]

[0076] in, τ(Θ,l) represents the time delay caused by the sound wave signal from direction Θ to microphone l; τ(v,k) represents the time delay caused by the sound wave signal from direction Θ to microphone k.

[0077] In this embodiment, a controllable response power-phase conversion SRP-PHAT fitness function can be determined based on the preprocessed acoustic wave signal and DOA estimation, wherein the SRP-PHAT fitness function represents the functional relationship between the drone position and power.

[0078] S140. Based on the candidate position, a preset optimization algorithm is used to iterate the SRP-PHAT fitness function to obtain the target UAV position.

[0079] The preset optimization algorithm may be an adaptive particle swarm optimization algorithm (APSO), which can be applied to the candidate positions generated by the TDOA method by applying the APSO algorithm. Through the particle swarm intelligent optimization search, the search range limitation in the prior art is avoided, the influence of the TDOA method error on the positioning accuracy is reduced, and the accuracy and reliability of the sound source positioning are improved. The target drone position may refer to the fitness value, i.e., P n(Θ) is the maximum corresponding to the UAV position. Specifically, the SRP-PHAT fitness function can be optimized and iterated based on the adaptive particle swarm optimization algorithm, and the target UAV position is obtained when the termination condition is met, wherein the termination condition may refer to the algorithm being terminated when the preset maximum number of iterations is reached or the fitness value has not been significantly improved for multiple consecutive iterations.

[0080] In this embodiment, a UAV sound wave signal is obtained and preprocessed; wherein the sound wave signal is collected by a microphone array; the position of the UAV is estimated based on the preprocessed sound wave signal based on a preset delay estimation algorithm to obtain multiple candidate positions; wherein the position is represented by the pitch angle and azimuth angle of the UAV; a controllable response power-phase conversion SRP-PHAT fitness function is determined based on the preprocessed sound wave signal; wherein the SRP-PHAT fitness function represents the functional relationship between the position and power of the UAV; based on the candidate position, the SRP-PHAT fitness function is optimized and iterated using a preset optimization algorithm to obtain the target UAV position, thereby solving the problems of inaccurate UAV sound source positioning and large calculation amount in the prior art, improving the accuracy and robustness of sound source positioning, and reducing the computational complexity.

[0081] Optionally, preprocessing the sound wave signal includes:

[0082] Calculating a cross power spectrum between two sound wave signals based on generalized cross correlation GCC, wherein the two sound wave signals are a sound wave signal pair, and the sound wave signals collected by the microphone array include a plurality of sound wave signal pairs;

[0083] Based on the phase conversion method PHAT, multiple cross power spectra are weighted and processed to obtain the preprocessed sound wave signal.

[0084] Among them, the generalized cross-correlation GCC may refer to a method for calculating the similarity between two sound wave signals, which can be used to analyze the cross-power spectrum between two sound wave signals. The specific GCC value calculation formula can be found in formula (4). In practical applications, the microphone array can effectively collect multiple sound wave signal pairs to achieve high-quality voice capture. However, since at least two microphone pairs located on the axis and the axis are required when estimating the DOA of the drone position by the TDOA method, at least three microphone array elements need to be set in the microphone array of this embodiment. The phase conversion method PHAT may refer to a signal processing technology for enhancing sound source localization and speech enhancement in multi-channel audio signals. Specifically, the phase conversion method (PHAT) can be used to perform phase transformation weighting on the microphone cross-correlation (GCC), normalize the amplitude of the cross-power spectrum, and realize pre-processing such as denoising, filtering and feature extraction of the sound wave signal collected by the microphone array; ensure the quality and reliability of the pre-processed sound wave signal, and provide a good foundation for subsequent algorithm processing.

[0085] In this embodiment, the generalized cross-correlation GCC can be used to calculate the mutual power spectrum of each pair of sound wave signals in the sound wave signal collected by the microphone array, and the phase transformation weighting of multiple mutual power spectra is performed based on the phase conversion method PHAT, and the amplitude of the mutual power spectrum is normalized to achieve preprocessing such as denoising, filtering and feature extraction of the sound wave signal collected by the microphone array; ensure the quality and reliability of the preprocessed sound wave signal, and provide a good foundation for subsequent algorithm processing.

[0086] Optionally, the drone position is estimated based on the preprocessed acoustic wave signal based on a preset delay estimation algorithm to obtain multiple candidate positions, including:

[0087] Determine the candidate position range according to the flight data of the UAV and the pre-processed UAV acoustic wave signal, wherein the flight data includes: the flight altitude and flight speed of the UAV;

[0088] A plurality of candidate positions are generated within the candidate position range based on a preset delay estimation algorithm.

[0089] The candidate position range may refer to the range of the candidate positions of the drone. Specifically, the flight altitude (H) and flight speed (V) of the drone may be obtained through GPS, sensors, and the like.

[0090] In this embodiment, the basic range of the candidate positions of the drone can be determined based on the flight data of the drone (such as altitude and speed) and the pre-processed drone sound wave signal, thereby ensuring the rationality of the candidate positions of the drone. Based on the TDOA method, a plurality of preliminary candidate positions of the drone are generated according to the time difference between the sound wave signals reaching different microphones, and a preliminary drone position estimate is provided, thereby ensuring the diversity and uniformity of the candidate positions of the drone.

[0091] Embodiment 2

[0092] Figure 2 This is a flow chart of a method for positioning a drone provided in Embodiment 2 of the present invention. The technical solution of this embodiment is further refined on the basis of the above embodiment. Figure 2 As shown, the method includes:

[0093] S210, obtaining a drone sound wave signal, and preprocessing the sound wave signal; wherein the sound wave signal is collected by a microphone array.

[0094] S220. Estimate the position of the drone based on the preprocessed sound wave signal based on a preset delay estimation algorithm to obtain multiple candidate positions; wherein the positions are represented by the pitch angle and azimuth angle of the drone.

[0095] S230. Determine a controllable response power-phase conversion SRP-PHAT fitness function based on the preprocessed acoustic wave signal, wherein the SRP-PHAT fitness function represents a functional relationship between the position and power of the drone.

[0096] S240 , selecting a preset number of target candidate positions from multiple candidate positions as an initial particle swarm based on the SRP-PHAT fitness function.

[0097] Among them, the preset number can be a preset number of particles. Specifically, when the number of candidate positions generated by TDOA is much larger than the required number of particles, in order not to increase the extra amount of calculation, it is necessary to select a preset number of target candidate positions from the candidate positions as the initial particle positions. Therefore, according to the fitness value of each candidate position calculated by the SRP-PHAT fitness function, a preset number of candidate positions can be selected as the target candidate positions, and the preset number of target candidate positions can be used as the initial particle positions to obtain an initial particle group.

[0098] S250: Based on the initial particle swarm, a preset optimization algorithm is used to iteratively optimize the SRP-PHAT fitness function to obtain the target UAV position.

[0099] Specifically, based on the obtained initial particle swarm, the initial particle swarm is initialized, specifically, the random speed v_i of all initial particles in the initial particle swarm is initialized, i=1,2,…,N_p; wherein N_p is a preset number. Based on the random speeds and initial particle positions of all initial particles in the initial particle swarm, the optimization process of the adaptive particle swarm optimization algorithm is started; the fitness value of each initial particle in the initial particle swarm calculated by the SRP-PHAT fitness function is used for subsequent iterative updates; in each iteration, the particle adjusts its position and speed according to the individual optimal position (pBest) and the global optimal position (gBest) until the termination condition is met, and the global optimal position, i.e., the target drone position, is output.

[0100] Among them, the individual best position (pBest) can refer to the position with the maximum fitness value encountered by each particle so far; in each iteration of the algorithm, the new position of each particle will be evaluated. If the fitness value of this position is better than the fitness value of the individual historical best position, then this position will become the new individual best position; the global best position (gBest) can refer to the position with the best fitness value explored by the entire particle group so far; in each iteration of the algorithm, the new positions of all particles will be evaluated. If the fitness value of the new position of a particle is better than the fitness value of the global historical best position, then this position will become the new global best position.

[0101] In this embodiment, a drone sound wave signal is obtained and preprocessed; wherein the sound wave signal is collected by a microphone array; the drone position is estimated based on the preprocessed sound wave signal based on a preset time delay estimation algorithm to obtain multiple candidate positions; wherein the position is represented by the pitch angle and azimuth angle of the drone; a controllable response power-phase conversion SRP-PHAT fitness function is determined based on the preprocessed sound wave signal, wherein the SRP-PHAT fitness function represents the functional relationship between the drone position and power; based on the SRP-PHAT fitness function, a preset number of target candidate positions are selected from multiple candidate positions as an initial particle group; based on the initial particle group, a preset optimization algorithm is used to iterate the SRP-PHAT fitness function to obtain the target drone position; the problems of inaccurate drone sound source positioning and large amount of calculation in the prior art are solved, the accuracy and robustness of sound source positioning are improved, and the calculation complexity is reduced.

[0102] Optionally, a preset number of target candidate positions are selected from multiple candidate positions based on the SRP-PHAT fitness function as an initial particle swarm, including:

[0103] Based on the SRP-PHAT fitness function, determine the fitness value corresponding to each candidate position; wherein the fitness value represents power information;

[0104] A preset number of target candidate positions are selected from a plurality of candidate positions in a descending order of the fitness values ​​as an initial particle group.

[0105] The power information may refer to the output power. Specifically, the fitness value corresponding to each candidate position may be calculated based on the SRP-PHAT fitness function formula (9), that is, the output power P n (Θ). A preset number of target candidate positions are selected from multiple candidate positions in descending order of the fitness values ​​corresponding to the candidate positions as initial particle positions, thereby obtaining an initial particle group.

[0106] In this embodiment, the fitness value corresponding to each candidate position can be determined according to the SRP-PHAT fitness function, and a preset number of target candidate positions are selected from multiple candidate positions in order from large to small according to the fitness values ​​corresponding to the candidate positions, as the initial particle positions, and then the initial particle swarm is obtained; this not only effectively saves the amount of calculation, but also can improve the accuracy and reliability of the UAV positioning results by fully searching the particle swarm in a larger range.

[0107] Optionally, based on the initial particle swarm, a preset optimization algorithm is used to iteratively optimize the SRP-PHAT fitness function to obtain the target UAV position, including:

[0108] Based on the fitness value of each particle in the initial particle swarm and the preset adaptive particle swarm optimization algorithm parameters, the SRP-PHAT fitness function is optimized and iterated according to the adaptive particle swarm optimization algorithm to obtain the target UAV position;

[0109] The adaptive particle swarm optimization algorithm parameters include: initial inertia weight, termination inertia weight, learning factor and maximum number of iterations.

[0110] Specifically, based on the initial particle swarm, the SRP-PHAT fitness function is optimized and iterated using a preset optimization algorithm, and the specific steps of obtaining the position of the target UAV include:

[0111] 1. Initialize the random speeds of all initial particles in the initial particle swarm; determine the positions of all initial particles in the initial particle swarm, that is, a preset number of target candidate positions; pre-set the adaptive particle swarm optimization algorithm parameters, including: initial inertia weight, termination inertia weight, learning factor and maximum number of iterations.

[0112] 2. Determine the fitness values ​​corresponding to all initial particles in the initial particle group according to the SRP-PHAT fitness function; compare the fitness value of each particle's current position with the fitness value of its historical optimal position. If the fitness value of the particle's current position is better than the fitness value of the individual historical optimal position, then this position becomes the new individual optimal position. Similarly, if the fitness value of the particle's current position is better than the fitness value of the global historical optimal position, then this position becomes the new global optimal position.

[0113] 3. Update the particle velocity according to the individual optimal position, the global optimal position and the adaptively adjusted parameters;

[0114] v i (t+1)=ω×v i (t)+c1×r1×(p best,i -x i (t))+c2×r2×(g best -x i (t)) (10)

[0115] Among them, r1 and r2 are random numbers between [0,1], p best,i is the individual optimal position of particle i, g best is the global optimal position; c1, c2 are learning factors, t is the current moment, and t+1 is the next moment.

[0116] Adjust the particle's position based on the velocity update result

[0117] x i (t+1)=x i (t)+v i (t+1) (11)

[0118] Adaptive parameter adjustment: In each iteration, the inertia weight ω is dynamically adjusted according to the current number of iterations.

[0119]

[0120] Among them, iter is the current iteration number, ω start Initial inertia weight, ω end Termination inertia weight, imax maximum number of iterations; when the number of iterations is close to the maximum number of iterations imax, the inertia weight gradually decreases, which can better converge to the optimal position.

[0121] 4. Termination condition: Determine whether the termination condition is met. If it is met, stop the iteration and output the result. When the preset maximum number of iterations is reached or the fitness function value has not been significantly improved for multiple consecutive iterations, the algorithm terminates. And output the global optimal position parameters as the target drone position.

[0122] In this embodiment, the SRP-PHAT fitness function is optimized and iterated according to the adaptive particle swarm optimization algorithm based on the fitness value of each particle in the initial particle swarm and the pre-set adaptive particle swarm optimization algorithm parameters to obtain the target UAV position; the APSO algorithm is applied on the basis of the candidate position generated by the TDOA method, and the global search capability of the particle swarm is used to fully search near the initial candidate position or even in a larger range to find the global optimal point of the UAV position, avoid the local optimal point, and effectively improve the accuracy of UAV sound source positioning.

[0123] Optionally, after obtaining the target UAV position, the method further includes: verifying the target UAV position and post-processing the target UAV position.

[0124] Specifically, the actual UAV position can be pre-set during the training phase or determined through other high-precision positioning equipment. After the target UAV position is obtained, a comparative analysis can be performed based on the predetermined actual UAV position and the target UAV position, and an error calculation can be performed. For example, an error threshold can be set. If the error value between the target UAV position and the actual UAV position is greater than the set error threshold, the target UAV position is determined to be inaccurate, the target UAV position is discarded, and the error value is recorded, and the root mean square error (RMSE) is calculated: the size of the error value is statistically evaluated, where RMSE is used to measure the overall performance of the positioning error.

[0125] For example, the actual drone position can be (x_true, y_true, z_true), and the target drone position can be (x_est, y_est, z_est), then the error value can be:

[0126]

[0127] Calculate the root mean square error: Among them, Δ i is the error of the ith comparative analysis, and n is the total number of comparative analyses. The largest error among multiple comparative analyses can be recorded to evaluate the positioning accuracy in the worst case. By analyzing the error value between the target drone position and the actual drone position, it can be determined whether the error comes from environmental noise, drone flight speed changes, or array design, and the main influencing factors causing the error can be determined.

[0128] If the error value between the target drone position and the actual drone position is less than or equal to the set error threshold, the target drone position is determined to be accurate. The AHL algorithm may refer to a hybrid positioning method (APSO-Based Hybrid Localization, AHL) optimized by a particle swarm optimization algorithm (APSO) based on adaptive weights and dynamically adjusted inertia weights; specifically, the hybrid positioning method is based on the TDOA method and the SRP-PHAT method; the embodiment of the present invention is mainly implemented by the AHL algorithm.

[0129] Among them, post-processing the position of the target drone may include: inertia weight adjustment, candidate point optimization, filtering and denoising, iterative verification, etc. Specifically, inertia weight adjustment may refer to adjusting the inertia weight in the AHL algorithm according to the error value, and the positioning accuracy may be improved by increasing or decreasing the weight. Candidate point optimization may refer to adjusting the candidate points generated by the TDOA algorithm, for example, by increasing the number of microphones, changing the array layout, etc., to improve the initial conditions of the sound source estimation. Filtering and denoising may refer to further processing the collected sound signal, for example, using a filter to remove environmental noise or other interference signals to improve the quality of the input data. Iterative verification may refer to comparing and analyzing the actual drone position with the target drone position for multiple times, calculating the error and optimizing it to ensure that the accuracy of the optimized AHL algorithm meets the preset accuracy requirements, for example, by gradually adjusting the parameters in the AHL algorithm to reduce the error value to within the preset accuracy range. To ensure the accuracy and reliability of the target drone position.

[0130] In this embodiment, during the training phase, after obtaining the target UAV position, the target UAV position can be verified and optimized. The specific verification and optimization is an iterative process. The accuracy of the AHL algorithm is gradually improved through error analysis, parameter adjustment, and result verification, thereby ensuring the accuracy and reliability of the target UAV position.

[0131] Optionally, the parameters of the AHL algorithm can be dynamically adjusted according to the flight data of the drone and the pre-processed drone sound wave signal to ensure the adaptability of the drone positioning under different flight conditions, and further ensure the stability and accuracy of the drone positioning. Specifically, if the flight altitude of the drone changes, the path length of the sound propagation will also be different, which may cause errors in the positioning of the sound source. The AHL algorithm in the embodiment of the present invention can adaptively adjust the range of the particle swarm search according to the change in the flight altitude of the drone, and can ensure the adaptability of the drone positioning under different flight conditions. If the speed of the drone increases, the time difference between the sound propagation and the capture will also change, which may cause the instability of the drone sound wave signal. The AHL algorithm in the embodiment of the present invention can dynamically adjust the inertia weight to increase the sensitivity to sound changes, thereby adapting to the positioning error caused by the rapid flight of the drone, and further ensuring the stability and accuracy of the drone positioning.

[0132] For example, Figure 3 This is a complete flow chart of a method for positioning a drone provided in Embodiment 2 of the present invention. Figure 3 As shown, the specific UAV positioning process based on the AHL algorithm can be:

[0133] 1. Collect drone acoustic signals through microphone array and pre-process them through GCC-PHAT (delay estimation) algorithm.

[0134] 2. Use the TDOA method to calculate the geometric relationship of the preprocessed acoustic wave signal (i.e., estimate the position of the drone) to obtain multiple candidate positions.

[0135] 3. Determine the controllable response power-phase conversion SRP-PHAT fitness function based on the preprocessed acoustic wave signal. Based on the SRP-PHAT fitness function, determine the fitness value corresponding to each candidate point; select a preset number of target candidate positions from multiple candidate points in descending order of the fitness value as initial particles, and then obtain an initial particle group.

[0136] 4. Initialize the particle swarm (i.e., initialize the initial particle swarm), initialize the random velocities of all initial particles in the initial particle swarm; and determine the positions of all initial particles in the initial particle swarm.

[0137] 5. Determine the global optimal position through the APSO algorithm as the target UAV position.

[0138] 6. Sound source DOA estimation outputs the target drone position.

[0139] In this embodiment, the DOA of the UAV position is roughly estimated by the TDOA method to determine several candidate positions, and then the initial particles of APSO are generated at the candidate positions, and then the SRP-PHAT fitness function is optimized by APSO to obtain its global optimal point, that is, the final target UAV position; the advantages of the TDOA algorithm and the SRP-PHAT algorithm are combined to significantly reduce the amount of calculation; through the intelligent optimization of the APSO algorithm, the accuracy of UAV positioning is effectively improved, thereby improving the operability of the present invention in practical applications.

[0140] Embodiment 3

[0141] Figure 4 This is a schematic diagram of the structure of a drone positioning device provided by Embodiment 3 of the present invention. Figure 4 As shown, the device comprises:

[0142] The acquisition module 310 is used to acquire the drone sound wave signal and pre-process the sound wave signal; wherein the sound wave signal is collected by a microphone array;

[0143] A first determination module 320 is used to estimate the position of the UAV based on the pre-processed acoustic wave signal based on a preset delay estimation algorithm to obtain a plurality of candidate positions, wherein the positions are represented by the pitch angle and azimuth angle of the UAV;

[0144] A second determination module 330 is used to determine a controllable response power-phase conversion SRP-PHAT fitness function based on the preprocessed acoustic wave signal, wherein the SRP-PHAT fitness function represents a functional relationship between the position and power of the drone;

[0145] The optimization module 340 is used to optimize and iterate the SRP-PHAT fitness function based on the candidate position using a preset optimization algorithm to obtain the target UAV position.

[0146] Optionally, the acquisition module 310 includes:

[0147] A calculation unit, used for calculating a cross power spectrum between two sound wave signals based on a generalized cross correlation GCC, wherein the two sound wave signals are a sound wave signal pair, and the sound wave signal collected by the microphone array includes a plurality of sound wave signal pairs;

[0148] The processing unit is used to perform weighted processing on multiple cross power spectra based on the phase conversion method PHAT to obtain a pre-processed sound wave signal.

[0149] Optionally, the first determining module 320 is specifically configured to:

[0150] Determine the candidate position range according to the flight data of the UAV and the pre-processed UAV acoustic wave signal, wherein the flight data includes: the flight altitude and flight speed of the UAV;

[0151] A plurality of candidate positions are generated within the candidate position range based on a preset delay estimation algorithm.

[0152] Optionally, the preset optimization algorithm is an adaptive particle swarm optimization algorithm.

[0153] Optionally, the optimization module 340 includes:

[0154] A selection unit, used for selecting a preset number of target candidate positions from a plurality of candidate positions as an initial particle swarm based on an SRP-PHAT fitness function;

[0155] The optimization unit is used to optimize and iterate the SRP-PHAT fitness function based on the initial particle swarm using a preset optimization algorithm to obtain the target UAV position.

[0156] Optionally, select a unit, specifically for:

[0157] Based on the SRP-PHAT fitness function, determine the fitness value corresponding to each candidate position; wherein the fitness value represents power information;

[0158] A preset number of target candidate positions are selected from a plurality of candidate positions in a descending order of the fitness values ​​as an initial particle group.

[0159] Optional, optimization unit, specifically for:

[0160] Based on the fitness value of each particle in the initial particle swarm and the preset adaptive particle swarm optimization algorithm parameters, the SRP-PHAT fitness function is optimized and iterated according to the adaptive particle swarm optimization algorithm to obtain the target UAV position;

[0161] The adaptive particle swarm optimization algorithm parameters include: initial inertia weight, termination inertia weight, learning factor and maximum number of iterations.

[0162] The drone positioning device provided in the embodiment of the present invention can execute the drone positioning method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0163] Embodiment 4

[0164] Figure 5A schematic diagram of an electronic device that can be used to implement an embodiment of the present invention is shown. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0165] like Figure 5 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program executable by the at least one processor 11, and the computer program is executed by the at least one processor 11 so that the at least one processor 11 can execute the method provided by the present invention.

[0166] The processor 11 can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 12 or a computer program loaded from a storage unit 18 into a random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0167] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0168] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a drone positioning method.

[0169] In some embodiments, the drone positioning method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the drone positioning method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the drone positioning method in any other appropriate manner (e.g., by means of firmware).

[0170] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard parts (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0171] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0172] In the context of the present invention, a computer-readable storage medium stores computer instructions, which are used to implement the drone positioning method provided by the present invention when the processor executes. A computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the above. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media may include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above.

[0173] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a cathode ray tube (CRT) or a liquid crystal display (LCD monitor)) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball), through which the user may provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0174] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by digital data communication (e.g., a communication network) in any form or medium. Examples of communication networks include: Local Area Network (LAN), Wide Area Network (WAN), blockchain network, and the Internet.

[0175] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and virtual private servers (VPS) services.

[0176] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0177] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for positioning a drone, characterized in that: include: Acquire a drone sound wave signal and pre-process the sound wave signal, wherein the sound wave signal is collected by a microphone array; The preprocessed acoustic wave signal is used to estimate the position of the UAV based on a preset delay estimation algorithm to obtain a plurality of candidate positions, wherein the positions are represented by the pitch angle and azimuth angle of the UAV; Determine a controllable response power-phase conversion SRP-PHAT fitness function based on the preprocessed acoustic wave signal, wherein the SRP-PHAT fitness function represents a functional relationship between the position and power of the UAV; Based on the candidate position, a preset optimization algorithm is used to optimize and iterate the SRP-PHAT fitness function to obtain the target UAV position.

2. The method according to claim 1, characterized in that Preprocessing the acoustic wave signal includes: Calculating a cross power spectrum between two sound wave signals based on generalized cross correlation GCC, wherein the two sound wave signals are a sound wave signal pair, and the sound wave signals collected by the microphone array include a plurality of sound wave signal pairs; Based on the phase conversion method PHAT, multiple cross power spectra are weighted and processed to obtain the preprocessed sound wave signal.

3. The method according to claim 1, characterized in that The pre-processed acoustic wave signal is used to estimate the position of the drone based on the preset delay estimation algorithm to obtain multiple candidate positions, including: Determine the candidate position range according to the flight data of the UAV and the pre-processed UAV acoustic wave signal, wherein the flight data includes: the flight altitude and the flight speed of the UAV; A plurality of candidate positions are generated within the candidate position range based on a preset delay estimation algorithm.

4. The method according to claim 1, characterized in that: The preset optimization algorithm is an adaptive particle swarm optimization algorithm.

5. The method according to claim 4, characterized in that Based on the candidate position, the SRP-PHAT fitness function is optimized and iterated using a preset optimization algorithm to obtain the target UAV position, including: Based on the SRP-PHAT fitness function, a preset number of target candidate positions are selected from multiple candidate positions as the initial particle swarm; Based on the initial particle swarm, a preset optimization algorithm is used to iterate the SRP-PHAT fitness function to obtain the target UAV position.

6. The method according to claim 5, characterized in that Based on the SRP-PHAT fitness function, a preset number of target candidate positions are selected from multiple candidate positions as the initial particle swarm, including: Based on the SRP-PHAT fitness function, determine the fitness value corresponding to each candidate position; wherein the fitness value represents power information; A preset number of target candidate positions are selected from a plurality of candidate positions in a descending order of the fitness values ​​as an initial particle group.

7. The method according to claim 5, characterized in that Based on the initial particle swarm, the SRP-PHAT fitness function is optimized and iterated using a preset optimization algorithm to obtain the target UAV position, including: Based on the fitness value of each particle in the initial particle swarm and the preset adaptive particle swarm optimization algorithm parameters, the SRP-PHAT fitness function is optimized and iterated according to the adaptive particle swarm optimization algorithm to obtain the target UAV position; The adaptive particle swarm optimization algorithm parameters include: initial inertia weight, termination inertia weight, learning factor and maximum number of iterations.

8. A drone positioning device, characterized in that: include: An acquisition module, used to acquire a drone sound wave signal and pre-process the sound wave signal; wherein the sound wave signal is collected by a microphone array; A first determination module is used to estimate the position of the UAV based on the pre-processed acoustic wave signal based on a preset delay estimation algorithm to obtain a plurality of candidate positions, wherein the positions are represented by the pitch angle and azimuth angle of the UAV; A second determination module is used to determine a controllable response power-phase conversion SRP-PHAT fitness function based on the preprocessed acoustic wave signal, wherein the SRP-PHAT fitness function represents a functional relationship between the position and power of the drone; The optimization module is used to optimize and iterate the SRP-PHAT fitness function based on the candidate position using a preset optimization algorithm to obtain the target UAV position.

9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the drone positioning method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to implement the drone positioning method described in claims 1-7 when executed by a processor.

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