A UAV positioning method, device, equipment and medium

Abstract

The embodiment of this specification discloses a method for positioning a drone, the method comprising: acquiring a drone signal, performing multipath elimination processing and Doppler effect elimination processing on the drone signal; positioning the drone according to the signal after the multipath elimination processing and Doppler effect elimination processing; wherein the multipath elimination processing comprises: determining the direction of the strongest path according to the SNR, filtering out the paths of the remaining directions except the direction of the strongest path, and obtaining the strongest path of the drone signal; the Doppler effect elimination processing comprises: calculating the Doppler bias according to the interval between two adjacent drone signal collections, and using the Doppler bias to eliminate the Doppler effect.

Description

Technical Field

[0001] The present invention relates to the field of drone technology, and more particularly to a method, device, equipment and medium for positioning a drone. Background Art

[0002] In the prior art, TDOA technology is generally used for passive positioning of drones. Among them, when measuring the distance from the drone to each positioning site, the distance difference between the drone and each measurement site in the time domain is simply considered, and then the weighted least squares method or Taylor series method is used to calculate the drone position. In addition, the particle swarm algorithm can be used to iterate multiple times to find the optimal drone position. In addition, when measuring the distance difference between the drone and each measurement site, the site error and the timing accuracy error between the sites are also considered.

[0003] However, the above-mentioned existing technologies mainly focus on the errors introduced by the position calculation method and the station site when determining the position of the drone, and do not consider the measurement errors introduced by the wireless channel environment, such as the error introduced by Doppler, the error introduced by multipath, the error introduced by multiple drone sources, etc. This results in the inability to control the processing of the measured signal source from the source, resulting in ranging errors and low drone positioning accuracy. Summary of the invention

[0004] The embodiments of this specification provide a method, device, equipment and medium for positioning a drone to solve the technical problem of how to improve the positioning accuracy of a drone.

[0005] To solve the above technical problems, the embodiments of this specification provide the following technical solutions:

[0006] This specification provides a method for positioning a drone, the method comprising:

[0007] Acquire a drone signal, and perform multipath elimination processing and Doppler effect elimination processing on the drone signal;

[0008] The UAV is positioned based on the signal after multipath elimination and Doppler effect elimination processing;

[0009] The multipath elimination process includes: determining the direction of the strongest path according to the SNR, filtering out the paths of the remaining directions except the direction of the strongest path, and obtaining the strongest path of the UAV signal;

[0010] The Doppler effect elimination process includes: calculating the Doppler bias according to the intervals between two adjacent drone signal collections, and using the Doppler bias to eliminate the Doppler effect.

[0011] Optionally, before performing multipath elimination processing, the method further includes:

[0012] Establish the UAV channel model and determine the null space vector;

[0013] According to the null space vector and the UAV channel model, a zero correlation filter corresponding to different SNRs is established.

[0014] Optionally, establishing zero correlation filters corresponding to different SNRs according to the null space vector and the UAV channel model includes:

[0015] According to different SNRs and the UAV system channel model, zero correlation filters corresponding to different SNRs are established; wherein the null space vector corresponding to any SNR is used as the coefficient of the zero correlation filter corresponding to the SNR.

[0016] Optionally, before performing multipath elimination processing, the method further includes:

[0017] Determine the background noise at the target location or area.

[0018] Optionally, determining the direction of the strongest path according to the SNR, filtering out paths in other directions except the direction of the strongest path, and obtaining the strongest path of the drone signal includes:

[0019] Performing a spectrum scan at a target location or target area to determine the current SNR in all directions based on the signal in the presence of the drone and the background noise;

[0020] The highest SNR is determined from the current SNRs of each orientation, and a zero-correlation filter corresponding to the highest SNR is used to filter out the paths of the remaining orientations except the orientation corresponding to the highest SNR, so as to obtain the strongest path of the drone signal.

[0021] Optionally, positioning the UAV based on the signal after multipath elimination processing and Doppler effect elimination processing includes:

[0022] Based on the signal after multipath elimination and Doppler effect elimination, the TDOA method is used to locate the UAV.

[0023] Optionally, the target location or target area includes a location where the drone positioning station is located;

[0024] or,

[0025] The target site or target area includes a location or area where drone prevention and control is required.

[0026] The present invention provides a drone positioning device, which includes:

[0027] A signal processing module, used for acquiring drone signals, and performing multipath elimination processing and Doppler effect elimination processing on the drone signals;

[0028] A signal positioning module is used to locate the UAV based on the signal after multipath elimination and Doppler effect elimination processing;

[0029] The multipath elimination process includes: determining the direction of the strongest path according to the SNR, filtering out the paths of the remaining directions except the direction of the strongest path, and obtaining the strongest path of the UAV signal;

[0030] The Doppler effect elimination process includes: calculating the Doppler bias according to the intervals between two adjacent drone signal collections, and using the Doppler bias to eliminate the Doppler effect.

[0031] The embodiment of this specification provides a drone positioning device, including:

[0032] at least one processor;

[0033] as well as,

[0034] a memory communicatively coupled to the at least one processor;

[0035] in,

[0036] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor, so that the at least one processor can execute the above-mentioned drone positioning method.

[0037] An embodiment of the present specification provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, the above-mentioned drone positioning method is implemented.

[0038] At least one of the above technical solutions adopted in the embodiments of this specification can achieve the following beneficial effects:

[0039] When positioning a drone, multipath elimination and Doppler effect elimination processing are performed on the drone signal in advance, and the processed signal is then used to position the drone. This can eliminate the measurement error caused by the wireless channel environment from the source and improve the positioning accuracy of the drone. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of this specification or the prior art, the drawings required for use in the embodiments of this specification or the prior art description are briefly described below. Obviously, the following only describes the drawings required for use in some embodiments of this application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor.

[0041] Figure 1It is a flowchart of the UAV positioning method provided in the first embodiment of this specification.

[0042] Figure 2 It is a schematic diagram of the filter establishment process in the first embodiment of this specification.

[0043] Figure 3 It is a structural schematic diagram of the UAV positioning device provided in the second embodiment of this specification. DETAILED DESCRIPTION

[0044] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments involved in the specific implementation methods are only part of the embodiments of this application, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the specific implementation methods without making creative work should belong to the scope of protection of this application.

[0045] In reality, even in an open rural environment, drone signals will be refracted by the ground, distant buildings, and diffracted and transmitted by trees, resulting in multipath effects on the signals received by the positioning station, which will cause small-scale attenuation.

[0046] In wireless communications, the generation of multipath effects is mainly related to the following factors:

[0047] reflection

[0048] When wireless signals encounter larger surfaces, they will be reflected, forming new signal paths. These reflected waves will arrive at the receiving end at the same time as the direct waves, and will cause interference and distortion due to different arrival delays and phases. For example, buildings, walls, ground, vehicles, etc. will reflect signals.

[0049] refraction

[0050] When wireless signals encounter a medium boundary during propagation, refraction occurs, changing the propagation path of the signal. At the same time, the signal reaching the receiving end will also experience a time delay and phase change. For example, in a city, refraction occurs when the signal passes through obstacles such as buildings and trees.

[0051] scattering

[0052] Scattering refers to the phenomenon that when wireless signals pass through an object, they are scattered by the irregular structures on the surface and inside the object, forming multiple new signal paths, which leads to signal distortion and interference. For example, in a city, scattering occurs when the signal passes through obstacles such as buildings and trees.

[0053] Reflection, refraction and scattering all refer to the reasons why drone signals change when they reach the positioning site.

[0054] When the drone is in a suburban or urban environment, large-scale and small-scale attenuation are obvious, and multipath fading is also more obvious.

[0055] The multipath effect will bring the following characteristics to wireless communication:

[0056] Signal attenuation Due to the existence of multiple signal paths, the signal strengths reaching the receiving end are different. Signal attenuation occurs after signal superposition, which reduces the signal strength received by the receiving end and affects the communication quality.

[0057] Signal distortion The multipath effect causes different arrival delays and phases of multiple signal paths, resulting in signal distortion. This causes the signal received by the receiver to be different from the signal sent by the transmitter, affecting the communication quality.

[0058] Signal interference multipath effect will produce multiple signal paths, some of which may overlap with signals from other wireless devices, causing signal interference and affecting communication quality.

[0059] Time-frequency broadening is caused by the multipath effect, which can lead to misalignment of signal arrival time and phase, causing the signal to change in the time domain and frequency domain, thereby broadening the signal and affecting the communication quality.

[0060] The superposition of signals from multiple drones causes the received signals to be variable.

[0061] If the original measurement data (i.e., the original UAV signal) is used for UAV positioning, the multipath effect will cause a large distance error between the site and the UAV, and the subsequent positioning algorithm will have reduced accuracy and slow convergence, resulting in low positioning accuracy.

[0062] The first embodiment of this specification (hereinafter referred to as "embodiment 1") provides a method for positioning a drone. The execution subject of embodiment 1 includes but is not limited to a terminal or a server or an operating system or an application program, that is, the execution subject can be various and can be set, used or changed as needed. In addition, a third-party application program can also assist the execution subject in executing embodiment 1.

[0063] In particular, the execution subject of the first embodiment may be a ground base station or a drone positioning device or other subject that can be used for drone positioning.

[0064] like Figure 1 As shown, the drone positioning method provided in the first embodiment includes:

[0065] S101: Acquire a drone signal, and perform multipath elimination processing and Doppler effect elimination processing on the drone signal;

[0066] The execution subject of the first embodiment can communicate with the drone and obtain the drone signal of the drone to be located. After obtaining the drone signal, multipath elimination processing and Doppler effect elimination processing are performed on the drone signal.

[0067] The multipath elimination process includes: determining the direction of the strongest path according to the SNR, filtering out the paths of the remaining directions except the direction of the strongest path, and obtaining the strongest path of the UAV signal;

[0068] The Doppler effect elimination process includes: calculating the Doppler bias according to the intervals between two adjacent drone signal collections, and using the Doppler bias to eliminate the Doppler effect.

[0069] The following are respectively described:

[0070] Multipath elimination processing

[0071] In the first embodiment, the multipath elimination process may include: determining the direction of the strongest path of the drone signal according to the SNR, filtering out the multipaths in other directions by filtering, and obtaining the strongest path of the drone signal, that is, the direct path.

[0072] Before performing multipath elimination processing, a drone channel model can be established to determine the null space vector; and based on the null space vector and the drone channel model, zero correlation filters corresponding to different SNRs can be pre-established.

[0073] The UAV communication system can be described by the following formula:

[0074] r = hs + n;

[0075] Where r represents the transmitted signal, h is the channel through which the signal passes, n is white noise, and s refers to the received wireless signal.

[0076] In general UAV communication systems, the conjugate of r point multiplication s does not change the homogeneity of each noise, so we get:

[0077] rxs*=(hs+n)s*→H=h+n;

[0078] Wherein, H represents the channel estimate.

[0079] In the first embodiment, the UAV system channel model may be established in a suitable manner, for example, as follows:

[0080] For actual drones or drone systems, there is no need to perform eigenvalue decomposition on the currently acquired drone signal (i.e., there is no need to perform eigenvalue decomposition on the drone signal in real time) to find the zero vector. Embodiment 1 proposes that the multipath column elements of channel h are uncorrelated, so that the elements of the h vector are uncorrelated, and the expectation of the matrix {h*h} (the matrix is ​​called the correlation matrix of h, and the matrix can be represented by E) can be approximated as a diagonal array. Further, Embodiment 1 proposes that the PDP (power delay profile) of the channel is a fixed value, such as the power obeys a decreasing exponential distribution, and the length of the channel does not exceed the CP length, so that a set of vectors can be generated offline and stored in a table, and the initial unfiltered channel at the pilot position is filtered to obtain the noise size.

[0081] According to the FFT characteristics, D=exp(-dl), where d can be preset as a constant value, and l is less than CPLen. Then the UAV system channel model established in Example 1 is the diagonal diag(D) of D.

[0082] In the first embodiment, the null space vector may be determined by a suitable method, for example, as follows:

[0083] In the first embodiment, by determining the vector w, arg min = {|E{|w H (Y+n)| 2 -σ 2 | 2}(This formula is recorded as Formula 1, where the received signal is Y+n and the noise power is σ 2 , then the zero vector w is such that {|E{|w H (Y+n)| 2 -σ 2 | 2} takes the smallest value. The vector w is called the zero vector or the null space vector.

[0084] The zero space noise detection method can be used to determine the vector w. Specifically, in a wireless signal environment, the signal and noise are naturally zero-correlated. H (Y+n)| 2 -σ 2 | 2 The partial derivative can be obtained, so that the minimum value of formula 1 satisfies: w H (E{YY H}+σ 2 I)w-σ 2 =0 (this formula is recorded as Formula 2). I is a matrix with all 1s on the diagonal, and σ refers to noise.

[0085] Since E{YYH} is a semi-positive definite matrix, E{YY H}+σ 2The minimum eigenvalue of I is σ 2 , then the eigenvalue is σ 2 The corresponding eigenvector (the eigenvector is the solution of Formula 2).

[0086] In the above manner, the zero vector or the null space vector can be determined.

[0087] In the first embodiment, zero correlation filters corresponding to different SNRs may be established in a suitable manner, for example, in the following manner:

[0088] In the first embodiment, establishing zero correlation filters corresponding to different SNRs according to the null space vector and the UAV channel model may include: establishing zero correlation filters corresponding to different SNRs according to different SNRs and the UAV system channel model. Wherein, any SNR can determine its corresponding null space vector as above, and use the null space vector corresponding to the SNR as the coefficient of the zero correlation filter corresponding to the SNR.

[0089] Generally, the SNR of a wireless channel ranges from -30 to 30, with a step of 0.2 dB. According to the SNR and the channel model, corresponding zero correlation filters (snr) can be established for different SNRs.

[0090] Figure 2 The process of establishing a zero correlation filter is shown. For any SNR, the eigenvalue decomposition of the autocorrelation matrix (i.e., the above-mentioned correlation matrix) is performed to obtain eigenvectors of different eigenvalues, and the eigensubspace corresponding to each eigenvector is obtained, thereby obtaining a zero correlation filter. The zero vector obtained above is used as the coefficient of the zero correlation filter.

[0091] The SNR described in the first embodiment is the signal-to-noise ratio, which is also called SNR or S / N (SIGNAL-NOISE RATIO) in English and refers to the ratio of the signal to the noise in an electronic device or electronic system. The measurement unit of the signal-to-noise ratio is dB, and the calculation method of the signal-to-noise ratio is SNR=10lg(PS / PN), where: SNR represents the signal-to-noise ratio, in dB; PS represents the effective power of the signal; PN represents the effective power of the noise.

[0092] In Embodiment 1, before performing multipath elimination processing, the background noise of the target location or target area can be determined. Specifically, the spectrum can be scanned in advance at the target location or target area, and the wireless background noise collected in the absence of a drone can be identified based on the spectrum, and used as Gaussian white noise, that is, background noise. The target location or target area can be determined as needed, for example, the target location or target area includes the location where the drone positioning site is located, thereby defining the location or area based on the location of the base station or site; obtaining the target location or target area includes the location or area where drone prevention and control is required.

[0093] In actual situations, determining the direction of the strongest path according to the SNR, filtering out the paths of the remaining directions except the direction of the strongest path, and obtaining the strongest path of the drone signal may include: performing spectrum scanning at the target location or target area, and determining the current SNR of each direction according to the signal in the presence of the drone (i.e., the drone signal obtained as described above, as the useful signal) and the background noise;

[0094] The highest SNR is determined from the current SNRs of all directions, and the zero-correlation filter corresponding to the highest SNR is used to filter out the paths of the directions other than the direction corresponding to the highest SNR, thereby filtering the wireless signal of the drone and obtaining the strongest path of the drone signal. This can be expressed as:

[0095] r = R * filter (snr);

[0096] In this way, various interferences (including irrelevant multipaths and noise other than the main path) can be filtered out, leaving only the main path (i.e., the strongest path or direct path).

[0097] Doppler effect elimination processing

[0098] The Doppler effect means that when a mobile station moves toward a base station, the frequency of the electromagnetic wave signal received by the mobile station becomes higher; when the mobile station moves away from the base station, the frequency of the signal received by the mobile station becomes lower. In actual situations, the flight speed of the drone will cause small-scale fading of the Doppler frequency deviation, especially when the speed of the drone is greater than 100 kilometers per hour, the Doppler frequency deviation is more significant.

[0099] In the first embodiment, the Doppler frequency deviation can be determined in advance based on the collection interval of two consecutive drone signals (the collection interval can be freely set). Adding the Doppler frequency deviation to the received original signal (i.e., the aforementioned drone signal obtained) can compensate for the signal frequency deviation and make subsequent positioning more accurate.

[0100] Specifically, assuming that the drone wireless signal is at frequency f0, the wave frequency f0 = c (speed of light) / λ (wavelength), and the drone moves toward the wave source at speed v. For the drone, the relative propagation speed of the wave is c + v, and the frequency of the wave received by the positioning station is fr = (c + v) λ, then the Doppler frequency shift fm = fr-f0 = vλ.

[0101] According to the above acquisition interval dtetaT = t1-t2 = fm = vλ, the UAV flight speed v = dtetaT / λ can be calculated. The Doppler frequency deviation fr can be directly calculated by the UAV flight speed using fr = (c+v)λ.

[0102] In the first embodiment, there is no absolute order between the multipath elimination process and the Doppler effect elimination process.

[0103] S103: Positioning the UAV based on the signal after multipath elimination processing and Doppler effect elimination processing.

[0104] In the first embodiment, after multipath elimination processing and Doppler effect elimination processing are performed on the drone signal, the signal after multipath elimination processing and Doppler effect elimination processing can be used for drone positioning, that is, drone positioning is performed based on the signal after multipath elimination processing and Doppler effect elimination processing.

[0105] Among them, positioning the UAV based on the signal after multipath elimination processing and Doppler effect elimination processing can include: positioning the UAV using TDOA or other suitable methods based on the signal after multipath elimination processing and Doppler effect elimination processing. For example, positioning the UAV using TDOA can specifically be to use the collected signals to determine the distance error between the UAV and each base station, and then use Taylor series or other mature algorithms to calculate the position of the UAV. In embodiment 1, since multipath elimination processing and Doppler effect elimination processing are performed in advance to obtain the processed signal, when the UAV is positioned using TDOA, the processed signal can be used to determine the distance error between the UAV and each base station, and then the Taylor series or other mature algorithms are used to calculate the position of the UAV.

[0106] Embodiment 1 can achieve the following beneficial effects:

[0107] When positioning the UAV, the UAV signal is pre-processed with front-end processing such as multipath elimination processing and Doppler effect elimination processing. The multipath elimination processing can filter the entire channel, remove multipaths and noise other than the main path, and determine the direct main path signal. The Doppler effect elimination processing can compensate for the Doppler bias of the UAV signal in real time. The UAV is positioned using the signal after multipath elimination processing and Doppler effect elimination processing, which can eliminate the measurement error caused by the wireless channel environment from the source, making the distance difference calculation from the UAV to the base station more accurate, effectively reducing the distance error between the UAV and each base station, and improving the positioning accuracy of the UAV.

[0108] like Figure 3 As shown, the second embodiment of this specification provides a drone positioning device corresponding to the method described in the first embodiment, and the device includes:

[0109] The signal processing module 202 is used to obtain the drone signal and perform multipath elimination processing and Doppler effect elimination processing on the drone signal;

[0110] The signal positioning module 204 is used to locate the UAV based on the signal after multipath elimination and Doppler effect elimination processing;

[0111] The multipath elimination process includes: determining the direction of the strongest path according to the SNR, filtering out the paths of the remaining directions except the direction of the strongest path, and obtaining the strongest path of the UAV signal;

[0112] The Doppler effect elimination process includes: calculating the Doppler bias according to the intervals between two adjacent drone signal collections, and using the Doppler bias to eliminate the Doppler effect.

[0113] Optionally, the signal processing module 202 is further used to establish a UAV channel model and determine a null space vector before performing multipath elimination processing;

[0114] According to the null space vector and the UAV channel model, a zero correlation filter corresponding to different SNRs is established.

[0115] Optionally, establishing zero correlation filters corresponding to different SNRs according to the null space vector and the UAV channel model includes:

[0116] According to different SNRs and the UAV system channel model, zero correlation filters corresponding to different SNRs are established; wherein the null space vector corresponding to any SNR is used as the coefficient of the zero correlation filter corresponding to the SNR.

[0117] Optionally, the signal processing module 202 is further configured to determine the background noise of the target location or target area before performing multipath elimination processing.

[0118] Optionally, determining the direction of the strongest path according to the SNR, filtering out paths in other directions except the direction of the strongest path, and obtaining the strongest path of the drone signal includes:

[0119] Performing a spectrum scan at a target location or target area to determine the current SNR in all directions based on the signal in the presence of the drone and the background noise;

[0120] The highest SNR is determined from the current SNRs of each orientation, and a zero-correlation filter corresponding to the highest SNR is used to filter out the paths of the remaining orientations except the orientation corresponding to the highest SNR, so as to obtain the strongest path of the drone signal.

[0121] Optionally, positioning the UAV based on the signal after multipath elimination processing and Doppler effect elimination processing includes:

[0122] Based on the signal after multipath elimination and Doppler effect elimination, the TDOA method is used to locate the UAV.

[0123] Optionally, the target location or target area includes a location where the drone positioning station is located;

[0124] or,

[0125] The target site or target area includes a location or area where drone prevention and control is required.

[0126] The third embodiment of this specification provides a drone positioning device, including:

[0127] at least one processor;

[0128] as well as,

[0129] a memory communicatively coupled to the at least one processor;

[0130] in,

[0131] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor, so that the at least one processor can execute the drone positioning method described in Example 1.

[0132] A fourth embodiment of the present specification provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, the drone positioning method described in the first embodiment is implemented.

[0133] The above embodiments can achieve the same technical effect, and the embodiments can be used in combination.

[0134] The above is only an embodiment of this specification and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for positioning a drone, characterized in that: The method comprises: Acquire a drone signal, and perform multipath elimination processing and Doppler effect elimination processing on the drone signal; The UAV is positioned based on the signal after multipath elimination and Doppler effect elimination processing; The multipath elimination process includes: determining the direction of the strongest path according to the SNR, filtering out the paths of the remaining directions except the direction of the strongest path, and obtaining the strongest path of the UAV signal; The Doppler effect elimination process includes: calculating the Doppler bias according to the intervals between two adjacent drone signal collections, and using the Doppler bias to eliminate the Doppler effect.

2. The method according to claim 1, characterized in that Before performing multipath elimination processing, the method further includes: Establish the UAV channel model and determine the null space vector; According to the null space vector and the UAV channel model, a zero correlation filter corresponding to different SNRs is established.

3. The method according to claim 2, characterized in that According to the null space vector and the UAV channel model, establishing zero correlation filters corresponding to different SNRs includes: According to different SNRs and the UAV system channel model, zero correlation filters corresponding to different SNRs are established; wherein the null space vector corresponding to any SNR is used as the coefficient of the zero correlation filter corresponding to the SNR.

4. The method according to claim 2 or 3, characterized in that Before performing multipath elimination processing, the method further includes: Determine the background noise at the target location or area.

5. The method according to claim 4, characterized in that Determine the direction of the strongest path according to the SNR, filter out the paths of other directions except the direction of the strongest path, and obtain the strongest path of the drone signal, including: Performing a spectrum scan at a target location or target area to determine the current SNR in all directions based on the signal in the presence of the drone and the background noise; The highest SNR is determined from the current SNRs of each orientation, and a zero-correlation filter corresponding to the highest SNR is used to filter out the paths of the remaining orientations except the orientation corresponding to the highest SNR, so as to obtain the strongest path of the drone signal.

6. The method according to claim 1 or 5, characterized in that The positioning of drones based on the signals after multipath elimination and Doppler effect elimination includes: Based on the signal after multipath elimination and Doppler effect elimination, the TDOA method is used to locate the UAV.

7. The method according to claim 4, characterized in that The target location or target area includes the location where the drone positioning station is located; or, The target site or target area includes a location or area where drone prevention and control is required.

8. A drone positioning device, characterized in that: The device comprises: A signal processing module, used for acquiring drone signals, and performing multipath elimination processing and Doppler effect elimination processing on the drone signals; A signal positioning module is used to locate the UAV based on the signal after multipath elimination and Doppler effect elimination processing; The multipath elimination process includes: determining the direction of the strongest path according to the SNR, filtering out the paths of the remaining directions except the direction of the strongest path, and obtaining the strongest path of the UAV signal; The Doppler effect elimination process includes: calculating the Doppler bias according to the intervals between two adjacent drone signal collections, and using the Doppler bias to eliminate the Doppler effect.

9. A drone positioning device, characterized in that: include: at least one processor; as well as, a memory communicatively coupled to the at least one processor; in, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor, so that the at least one processor can execute the drone positioning method described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, the drone positioning method according to any one of claims 1 to 7 is implemented.

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