Multi-station positioning method and system based on TDOA and AOA

Abstract

The present invention provides a multi-station positioning method and system based on TDOA and AOA, using K antenna base stations to locate a UAV, at least one antenna base station includes two or more receiving antennas, a two-dimensional plane coordinate system is established through the first target data generated by each antenna when receiving a target signal, and the coordinates of each antenna base station are obtained; based on the range and step length of the two-dimensional plane coordinate system, a number of assumed coordinates corresponding to the target signal sent by the UAV are obtained; based on the first target data and the coordinates of each antenna base station, the cumulative value of the cross-correlation power spectrum corresponding to each assumed coordinate is obtained; based on the cumulative value of the cross-correlation power spectrum corresponding to each assumed coordinate, the position coordinates of the UAV are determined. The present invention searches for the position of the UAV by establishing a coordinate system and a number of assumed coordinates, and the positioning accuracy obtained is higher, and no actual sensor elements are required, so that the present invention has a lower hardware cost and a simpler industrial implementation.

Description

Technical Field

[0001] The present invention relates to the field of multi-station positioning of unmanned aerial vehicles, and in particular to a multi-station positioning method and system based on TDOA and AOA. Background Art

[0002] Multi-station positioning systems are widely used in specific applications such as military reconnaissance and surveillance, agriculture and environmental monitoring due to their high accuracy, reliability and wide coverage.

[0003] UAV multi-station positioning is a method of determining the position of a UAV through multiple ground stations or antenna base stations. The existing multi-station positioning technology often uses multiple stations to receive the signals sent by the UAV, calculates the signal strength difference, arrival time difference and arrival angle of the signal, and thus locates the position of the UAV. This hybrid positioning can significantly improve the positioning accuracy and enhance the robustness and reliability of the system in complex environments with multipath effects and signal shielding.

[0004] However, since this technology involves the integration of multiple multi-station positioning technologies, the hybrid positioning system requires more antenna base stations and sensor devices. For example, TDOA positioning requires equipment to obtain time difference information between different antenna base stations, and AOA positioning requires direction finding equipment to obtain the AOA information of the signal. This makes hybrid positioning require higher hardware costs and more complex industrial implementation.

[0005] Therefore, how to ensure the positioning accuracy of drones while ensuring that the multi-station positioning system has lower hardware cost and simpler industrial implementation has become a technical problem that needs to be urgently solved in the industry. Summary of the invention

[0006] The present invention provides a multi-station positioning method and system based on TDOA and AOA, which solves the technical problem of how to ensure the positioning accuracy of unmanned aerial vehicles while ensuring that the multi-station positioning system has lower hardware cost and simpler industrial implementation.

[0007] According to a first aspect of the present invention, an embodiment of the present invention provides a multi-station positioning method based on TDOA and AOA, using K antenna base stations to locate a drone, at least one of the antenna base stations includes two or more receiving antennas, and K is an integer greater than or equal to 2;

[0008] The multi-station positioning method includes:

[0009] Each receiving antenna receives a signal within its measurement range respectively, and if a target signal is received, generates first target data, wherein the first target data includes time domain information of the target signal;

[0010] Based on all the first target data, a two-dimensional plane coordinate system is established, and coordinates of each antenna base station are obtained, where the coordinates of each antenna base station are coordinates on the two-dimensional plane coordinate system;

[0011] Based on the range and step size of the two-dimensional plane coordinate system, a number of assumed coordinates corresponding to when the UAV sends the target signal are obtained;

[0012] Based on the first target data and the coordinates of each antenna base station, obtaining a cross-correlation power spectrum accumulation value corresponding to each assumed coordinate;

[0013] Determine the position coordinates of the UAV based on the accumulated value of the cross-correlation power spectrum corresponding to each assumed coordinate;

[0014] The assumed coordinates corresponding to the maximum cross-correlation power spectrum accumulation value are the position coordinates.

[0015] Optionally, obtaining the accumulated value of the cross-correlation power spectrum corresponding to the assumed coordinates includes:

[0016] Based on the assumed coordinates and the coordinates of each antenna base station, obtaining the incoming wave direction angle of the target signal corresponding to the antenna base station and the relative distance of the UAV corresponding to each antenna base station;

[0017] Based on the arrival direction angle of the target signal corresponding to each antenna base station, performing a first processing on all the first target data corresponding to each antenna base station to generate corresponding second target data, wherein the first processing includes a deep fusion processing and an azimuth compensation processing;

[0018] Based on the incoming wave direction angle of the target signal corresponding to each antenna base station and the relative distance of the UAV corresponding to each antenna base station, determine the relative time difference between the antenna base stations;

[0019] Based on the relative time difference between the antenna base stations and each of the second target data, obtaining an absolute value of a cross-power spectrum integral between the antenna base stations after time difference compensation;

[0020] All the absolute values ​​of the cross-power spectrum integrals corresponding to each of the assumed coordinates are accumulated to generate a cross-correlation power spectrum accumulation value.

[0021] Optionally, the range of the two-dimensional plane coordinate system includes the range of the first direction x∈[x low ,x high ] and the step length x in the first direction step , and the range of the second direction y∈[y low ,y high ] and the step length y in the second direction step , and the first direction is perpendicular to the second direction;

[0022] Based on the range and step size of the two-dimensional plane coordinate system, several assumed coordinates corresponding to when the drone sends the target signal are obtained, including:

[0023] Based on the range and step size of the two-dimensional plane coordinate system, determine the M×N two-dimensional regional network Ξ, where M=(x high -x low ) / x step +1, N = (y high -y low ) / y step +1;

[0024] Based on all grid cells in the two-dimensional area network, the assumed coordinates corresponding to each grid cell are determined.

[0025] Optionally, based on the grid unit Ξ(m,n) in the two-dimensional area network, determining the assumed coordinates (x,y) corresponding thereto includes:

[0026] Determine the assumed coordinates (x, y) corresponding to the grid unit Ξ(m, n) based on the step size corresponding to the grid unit Ξ(m, n), and

[0027]

[0028] Here, m and n are both integers, and 0≤m≤M-1, 0≤n≤N-1.

[0029] Optionally, the antenna base station S with index number k k The first target data corresponding to the pth receiving antenna:

[0030]

[0031] Among them, s(t) is the time domain signal transmitted by the UAV, h k For drones and antenna base stations S k The channel gain on the path, τ k represents the delay caused by the path, Represents the antenna base station S k The signal direction angle function of the p-th receiving antenna, and 0≤k≤K-1, p≥1.

[0032] Optionally, antenna base station S k Second target data

[0033]

[0034] Among them, θ k For the drone at the antenna base station S k The incoming wave direction angle, and θ k=arctan(x k ,yy k );

[0035] l k The UAV corresponds to the antenna base station S k The relative distance of

[0036] N k is the antenna base station S k The total number of receiving antennas.

[0037] Optionally, the K antenna base stations form an equally spaced circular antenna array.

[0038] Optionally, antenna base station S k The wave direction angle function

[0039] in, R k is the antenna base station S k The radius of the antenna array, is the antenna base station S k is the position angle of the pth receiving antenna, and λ represents the wavelength of the carrier.

[0040] Optionally, the antenna base station S with index number k k The antenna base station S with index number i i The relative time difference between

[0041]

[0042] Among them, i≠k, and 0≤i≤K-1.

[0043] Optionally, antenna base station S k With antenna base station S i The absolute value of the cross power spectrum integral after time difference compensation

[0044]

[0045] Where, s(w) is the target signal in the frequency domain, is the antenna base station S k and antenna base station S i Phase deviation caused by clock asynchrony, is a constant.

[0046] According to a second aspect of the present invention, an embodiment of the present invention provides a multi-station positioning system based on TDOA and AOA, including:

[0047] K antenna base stations, used to locate the UAV, at least one of the antenna base stations includes two or more receiving antennas, and K is an integer greater than or equal to 2;

[0048] A target signal receiving module, used to use each receiving antenna to receive a signal within its measurement range respectively, and if a target signal is received, generate first target data, wherein the first target data includes time domain information of the target signal;

[0049] A coordinate system establishment module, used to establish a two-dimensional plane coordinate system based on all the first target data, and obtain the coordinates of each antenna base station, where the coordinates of each antenna base station are all coordinates on the two-dimensional plane coordinate system;

[0050] An assumed coordinate acquisition module, used for obtaining a number of assumed coordinates corresponding to when the UAV sends the target signal based on the range and step size of the two-dimensional plane coordinate system;

[0051] A cross-correlation power spectrum cumulative value acquisition module, used to obtain a cross-correlation power spectrum cumulative value corresponding to each assumed coordinate based on the first target data and the coordinates of each antenna base station;

[0052] A drone position determination module, used to determine the position coordinates of the drone based on the accumulated value of the cross-correlation power spectrum corresponding to each assumed coordinate;

[0053] The assumed coordinates corresponding to the maximum cross-correlation power spectrum accumulation value are the position coordinates.

[0054] Optionally, the cross-correlation power spectrum cumulative value acquisition module includes:

[0055] An incoming wave direction angle and relative distance calculation unit is used to obtain the incoming wave direction angle of the target signal corresponding to the antenna base station and the relative distance of the UAV corresponding to each antenna base station based on the assumed coordinates and the coordinates of each antenna base station;

[0056] A first processing unit, configured to perform a first processing on all the first target data corresponding to each antenna base station based on the arrival direction angle of the target signal corresponding to each antenna base station to generate corresponding second target data, wherein the first processing includes a deep fusion processing and an azimuth compensation processing;

[0057] A relative time difference calculation unit, used to determine the relative time difference between the antenna base stations based on the incoming wave direction angle of the target signal corresponding to each antenna base station and the relative distance of the UAV corresponding to each antenna base station;

[0058] A cross-power spectrum integral absolute value calculation unit, used to obtain a cross-power spectrum integral absolute value between the antenna base stations after time difference compensation based on the relative time difference between the antenna base stations and each of the second target data;

[0059] The cross-correlation power spectrum cumulative value calculation unit is used to accumulate all the cross-power spectrum integral absolute values ​​corresponding to each of the assumed coordinates to generate a cross-correlation power spectrum cumulative value.

[0060] Compared with the prior art, the technical solution of the embodiment of the present invention has the following beneficial effects:

[0061] In the multi-station positioning method and multi-station positioning system of the technical solution of the present invention, K antenna base stations are used to locate the UAV, at least one antenna base station includes two or more receiving antennas, and a two-dimensional plane coordinate system is established through the first target data generated by each antenna when receiving the target signal, and the coordinates of each antenna base station are obtained; based on the range and step size of the two-dimensional plane coordinate system, a number of assumed coordinates corresponding to the target signal sent by the UAV are obtained; based on the first target data and the coordinates of each antenna base station, the cumulative value of the cross-correlation power spectrum corresponding to each assumed coordinate is obtained; based on the cumulative value of the cross-correlation power spectrum corresponding to each assumed coordinate, the position coordinates of the UAV are determined. The present invention searches for the position of the UAV by establishing a coordinate system and a number of assumed coordinates, and the obtained positioning accuracy is higher, and no actual sensor elements are required, so that the present invention has a lower hardware cost and a simpler industrial implementation.

[0062] Furthermore, the present invention uses the assumed coordinates and the coordinates of each antenna base station to obtain the arrival direction angle of the target signal corresponding to the antenna base station, and the relative distance of the drone corresponding to each antenna base station; it can be seen that the present invention does not need to use actual sensor elements for direction finding and delay measurement, and saves hardware costs by using the method of assumed position.

[0063] Furthermore, using the arrival direction angle of the target signal corresponding to each antenna base station, all the first target data corresponding to each antenna base station are first processed to generate the corresponding second target data, and the first processing includes deep fusion processing and azimuth compensation processing; based on the arrival direction angle of the target signal corresponding to each antenna base station, and the relative distance of the drone corresponding to each antenna base station, the relative time difference between the antenna base stations is determined; based on the relative time difference between the antenna base stations and each second target data, the absolute value of the mutual power spectrum integral between the antenna base stations after time difference compensation is obtained; all the absolute values ​​of the mutual power spectrum integral corresponding to each assumed coordinate are accumulated to generate the cumulative value of the cross-correlation power spectrum. It can be seen that the present invention can be applied to various scenarios by calculating the absolute value of the mutual power spectrum integral between antenna base stations using the arrival angle and time difference information, and the performance is better and the adaptability is stronger. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 paying creative work.

[0065] Figure 1 is a schematic diagram of a system model for multi-station positioning of a drone in one embodiment of the present invention;

[0066] Figure 2 is a flow chart of a multi-station positioning method in one embodiment of the present invention;

[0067] Figure 3 is a schematic diagram of a two-dimensional plane coordinate system in one embodiment of the present invention;

[0068] Figure 4 is a schematic diagram of a process for obtaining the accumulated value of the cross-correlation power spectrum corresponding to the assumed coordinates in one embodiment of the present invention;

[0069] Figure 5 is a block diagram of a multi-station positioning system in one embodiment of the present invention;

[0070] Figure 6 It is a block diagram of a module for acquiring a cross-correlation power spectrum accumulation value in one embodiment of the present invention.

[0071] Description of reference numerals:

[0072] 61-antenna base station;

[0073] 62-target signal receiving module;

[0074] 63-coordinate system establishment module;

[0075] 64-assumed coordinate acquisition module;

[0076] 65- cross-correlation power spectrum cumulative value acquisition module;

[0077] 66-UAV location determination module;

[0078] 651-incoming wave direction angle and relative distance calculation unit;

[0079] 652-first processing unit;

[0080] 653-Relative time difference calculation unit;

[0081] 654-cross power spectrum integral absolute value calculation unit;

[0082] 655- Cross-correlation power spectrum accumulation value calculation unit. DETAILED DESCRIPTION

[0083] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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 are within the scope of protection of the present invention.

[0084] The terms "first", "second", "third", "fourth", etc. (if any) 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.

[0085] The technical solution of the present invention is described in detail with specific embodiments below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0086] As described in the background art, it is currently difficult to ensure the positioning accuracy of the UAV while taking into account the low hardware cost and simple industrial implementation of the multi-station positioning system.

[0087] At present, the common multi-station positioning methods are mainly the following:

[0088] 1) Received signal strength positioning:

[0089] Since the signal strength will be affected in the propagation path during the signal transmission process, the signal can be located based on the strength of the received signal. In the case of a known signal propagation model (such as a spatial path loss model), the received signal strength can be compared with the theoretical strength under the known model to estimate the distance to the target. However, in practical applications, the method based on the received signal strength measurement is greatly affected by environmental factors such as the multipath effect, which reduces the positioning effect.

[0090] 2) TDOA positioning:

[0091] Assuming that the distance between antenna base stations is known, the relative position of the drone is determined by calculating the TDOA of the signal between the antenna base stations. TDOA positioning has high accuracy and does not depend on signal strength. It is suitable for dynamic targets, but highly synchronized clocks are required between multiple antenna base stations to ensure accurate measurement of time differences.

[0092] 3) AOA positioning:

[0093] Each antenna base station is equipped with one or more directional antennas or antenna arrays that can measure the AOA of the received signal. Relying on the geometric relationship of the signal transmission path, this method can determine the location of the signal source through the intersection of angles measured by multiple antenna base stations. Compared with TDOA positioning, AOA positioning does not require clock synchronization and is insensitive to signal strength, but this solution has higher requirements for the measurement angle accuracy and requires more computing resources.

[0094] However, none of the above methods can achieve reliable and accurate multi-station positioning. Therefore, existing multi-station positioning often uses multiple stations to receive signals from drones, calculate the signal strength difference, TDOA positioning, and AOA positioning hybrid positioning methods to locate the position of drones. This hybrid positioning can significantly improve positioning accuracy and enhance the robustness and reliability of the system in complex environments with multipath effects and signal shielding.

[0095] However, the hybrid positioning of existing technologies requires higher hardware costs and more complex industrial implementations. For example, TDOA positioning requires equipment to obtain time difference information between different antenna base stations, and AOA positioning requires direction-finding equipment to obtain the AOA information of the signal.

[0096] In view of this, the present invention provides a multi-station positioning method, which uses K antenna base stations to locate the UAV, at least one antenna base station includes two or more receiving antennas, and establishes a two-dimensional plane coordinate system through the first target data generated by each antenna when receiving the target signal, and obtains the coordinates of each antenna base station; based on the range and step size of the two-dimensional plane coordinate system, obtains a number of assumed coordinates corresponding to the target signal sent by the UAV; based on the first target data and the coordinates of each antenna base station, obtains the cumulative value of the cross-correlation power spectrum corresponding to each assumed coordinate; based on the cumulative value of the cross-correlation power spectrum corresponding to each assumed coordinate, determines the position coordinates of the UAV. The present invention searches for the position of the UAV by establishing a coordinate system and a number of assumed coordinates, and the obtained positioning accuracy is higher, and no actual sensor elements are required, so that the present invention has a lower hardware cost and a simpler industrial implementation.

[0097] In order to make the above-mentioned objects, features and beneficial effects of the present invention more obvious and easy to understand, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0098] The embodiment of the present invention provides a multi-station positioning method based on TDOA and AOA, which uses K antenna base stations to locate the UAV, at least one of the antenna base stations includes two or more receiving antennas, and K is an integer greater than or equal to 2;

[0099] As an example, the system model of the multi-station positioning of the UAV of the present invention can be as follows Figure 1 As shown, where k is the index of the antenna base station, l k is the antenna base station S k The distance from the drone, θ k is the antenna base station S k The direction angle of the incoming signal received at k is the antenna base station S k The total number of receiving antennas, and 0≤k≤K-1.

[0100] It should be understood that the present invention does not limit the arrangement of antenna base stations and the number of antennas in each antenna base station, and those skilled in the art can select a suitable device arrangement as needed.

[0101] Please refer to Figure 2 , the multi-station positioning method includes:

[0102] S1: Each receiving antenna receives a signal within its measurement range respectively. If a target signal is received, first target data is generated. The first target data includes time domain information of the target signal.

[0103] S2: Based on all the first target data, a two-dimensional plane coordinate system is established, and coordinates of each antenna base station are obtained, where the coordinates of each antenna base station are coordinates on the two-dimensional plane coordinate system;

[0104] S3: Based on the range and step size of the two-dimensional plane coordinate system, obtain a number of assumed coordinates corresponding to when the UAV sends the target signal;

[0105] S4: based on the first target data and the coordinates of each antenna base station, obtaining a cross-correlation power spectrum accumulation value corresponding to each assumed coordinate;

[0106] S5: Determine the position coordinates of the UAV based on the accumulated value of the cross-correlation power spectrum corresponding to each assumed coordinate;

[0107] The assumed coordinates corresponding to the maximum cross-correlation power spectrum accumulation value are the position coordinates.

[0108] It can be seen that the present invention uses a multi-antenna fusion method to achieve multi-station positioning of the UAV. Since the angles and paths of the wave directions of the signals sent by the UAV to different antenna base stations are different, if the antenna base station can obtain the relative position of the UAV, the antenna base station can use this position to calculate the corresponding AOA information and TDOA information, and use the relevant information to deeply fuse the received signals of the multiple antenna base stations to restore the signals and compensate for the time difference. On this basis, by comparing the cumulative value of the absolute value of the integral of the cross-correlation power spectrum of the received signals of the UAV at different positions within a certain area, accurate positioning within a certain area can be achieved.

[0109] The method of the present invention is now further described. The following antenna base station S k , which can be understood as the antenna base station with index number k; similarly, the antenna base station S i , which can be understood as the antenna base station with index number i.

[0110] Please refer to Figure 2 , execute step S1,

[0111] S1: Each receiving antenna receives a signal within its measurement range respectively. If a target signal is received, first target data is generated. The first target data includes time domain information of the target signal.

[0112] As an example, the antenna base station S with index number k k The first target data corresponding to the pth receiving antenna:

[0113]

[0114] Among them, s(t) is the time domain signal transmitted by the UAV, h kFor drones and antenna base stations S k The channel gain on the path, τ k represents the delay caused by the path, Represents the antenna base station S k The signal direction angle function of the p-th receiving antenna, and 0≤k≤K-1, p≥1.

[0115] In a preferred embodiment, the present invention also pre-processes the target signal, such as denoising (such as Wiener filtering, wavelet transform), etc. Otherwise, the integral value of the cross-correlation power spectrum will be affected to a certain extent. Of course, when the number of antennas in the antenna base station is large enough or the noise power is small, denoising can be ignored, and the influence of noise can be resisted by multi-antenna deep fusion.

[0116] Please refer to Figure 2 as well as Figure 3 , execute step S2,

[0117] S2: Based on all the first target data, a two-dimensional plane coordinate system is established, and the coordinates of each antenna base station are obtained, and the coordinates of each antenna base station are all coordinates on the two-dimensional plane coordinate system.

[0118] Since the relative position of the drone and the placement positions of all antenna base stations are known, the present invention can obtain the position coordinates of the drone and different antenna base stations. In one embodiment, the established two-dimensional plane coordinate system can be as follows: Figure 3 As shown, where k is the index of the antenna base station, l k is the antenna base station S k and the signal propagation path distance of the UAV, θ k is the antenna base station S k The direction angle of the incoming signal received at the drone is assumed to be (x, y), and the antenna base station S k The position coordinates are (x k ,y k ).

[0119] Please refer to Figure 2 , execute step S3,

[0120] S3: Based on the range and step size of the two-dimensional plane coordinate system, a number of assumed coordinates corresponding to when the UAV sends the target signal are obtained.

[0121] In a specific implementation manner, the range of the two-dimensional plane coordinate system includes the range of the first direction x∈[x low ,x high ] and the step length x in the first direction step , and the range of the second direction y∈[y low ,y high ] and the step length y in the second direction step, and the first direction is perpendicular to the second direction;

[0122] Then, in step S3, based on the range and step length of the two-dimensional plane coordinate system, a number of assumed coordinates corresponding to when the drone sends the target signal are obtained, including:

[0123] Based on the range and step size of the two-dimensional plane coordinate system, determine the M×N two-dimensional regional network Ξ, where M=(x high -x low ) / x step +1, N = (y high -y low ) / y step +1;

[0124] Based on all grid cells in the two-dimensional area network, the assumed coordinates corresponding to each grid cell are determined.

[0125] For example, the range of the drone position search is set to x∈[0,5km], y∈[0,5km], and the corresponding range of the two-dimensional plane coordinate system is also set to x∈[0,5km], y∈[0,5km]. In this case, a certain step size x step =100m and y step =100m, we can divide this area into a regional grid Ξ with a dimension of 51×51 (M×N):

[0126] Ξ(m,n)=(m×100,n×100),m∈[0,M-1]; n∈[0,N-1]

[0127] In one implementation, based on the grid unit Ξ(m,n) in the two-dimensional area network, determining the assumed coordinates (x, y) corresponding thereto includes:

[0128] Determine the assumed coordinates (x, y) corresponding to the grid unit Ξ(m, n) based on the step size corresponding to the grid unit Ξ(m, n), and

[0129]

[0130] Here, m and n are both integers, and 0≤m≤M-1, 0≤n≤N-1.

[0131] Please refer to Figure 2 as well as Figure 4 , execute step S4,

[0132] S4: based on the first target data and the coordinates of each antenna base station, obtaining a cross-correlation power spectrum accumulation value corresponding to each assumed coordinate;

[0133] In one implementation, please refer to Figure 4, step S4 of obtaining the accumulated value of the cross-correlation power spectrum corresponding to the assumed coordinates includes:

[0134] S41: Based on the assumed coordinates and the coordinates of each antenna base station, obtaining the incoming wave direction angle of the target signal corresponding to the antenna base station and the relative distance of the drone corresponding to each antenna base station;

[0135] Specifically, based on the assumed coordinates (x, y) of the drone, it can be estimated that the drone is at the antenna base station S at this time k The incoming wave direction angle θ k and the relative distance l k :

[0136]

[0137] It can be seen that the present invention uses the assumed coordinates and the coordinates of each antenna base station to obtain the arrival direction angle of the target signal corresponding to the antenna base station, and the relative distance of the drone corresponding to each antenna base station; it can be seen that the present invention does not need to use actual sensor elements for direction finding and delay measurement, and saves hardware costs by using the method of assumed position.

[0138] S42: Based on the arrival direction angle of the target signal corresponding to each antenna base station, perform a first processing on all the first target data corresponding to each antenna base station to generate corresponding second target data, wherein the first processing includes a deep fusion processing and an azimuth compensation processing;

[0139] Specifically, the antenna base station S k Second target data

[0140]

[0141] Among them, N k is the antenna base station S k The total number of receiving antennas;

[0142] Among them (·) * represents the conjugate operation, Indicates the antenna base station S k The multi-antenna deep fusion gain is a constant.

[0143] In a preferred embodiment, the K antenna base stations form an equally spaced circular antenna array.

[0144] In this case, the antenna base station S k The wave direction angle function

[0145] in, R k is the antenna base station S kThe radius of the antenna array, is the antenna base station S k is the position angle of the pth receiving antenna, and λ represents the wavelength of the carrier.

[0146] S43: Determine the relative time difference between the antenna base stations based on the incoming wave direction angle of the target signal corresponding to each antenna base station and the relative distance of the drone corresponding to each antenna base station;

[0147] As an example, the antenna base station S with index number k k The antenna base station S with index number i i The relative time difference between

[0148]

[0149] Among them, i≠k, and 0≤i≤K-1.

[0150] S44: based on the relative time difference between the antenna base stations and each of the second target data, obtaining an absolute value of a cross-power spectrum integral between the antenna base stations after time difference compensation;

[0151] As an example, the antenna base station S k With antenna base station S i The absolute value of the cross power spectrum integral after time difference compensation

[0152]

[0153] Where, s(w) is the target signal in the frequency domain, is the antenna base station S k and antenna base station S i Phase deviation caused by clock asynchrony, is a constant.

[0154] It can be seen that the system uses the estimated TDOA information τ k,i The time difference compensation of the cross-correlation power spectrum improves the robustness of the multi-station positioning system.

[0155] In summary, the present invention performs time difference compensation on the absolute value of the cross-power spectrum integral between antenna base stations through the arrival angle and time difference information, so that the method of the present invention can be applied in various scenarios with better performance and stronger adaptability.

[0156] S45: Accumulate all the absolute values ​​of the cross-power spectrum integrals corresponding to each of the assumed coordinates to generate a cross-correlation power spectrum accumulation value.

[0157] Specifically, when the coordinates of the drone are assumed to be (x, y), the P of all antenna base stations Sk k,i The cumulative result is:

[0158]

[0159] Please refer to Figure 2 , execute step S5,

[0160] S5: Determine the position coordinates of the UAV based on the cumulative value of the cross-correlation power spectrum corresponding to each assumed coordinate; wherein the assumed coordinate corresponding to the maximum cumulative value of the cross-correlation power spectrum is the position coordinate.

[0161] In one implementation, step S5 includes:

[0162] Traverse all the assumed coordinates of the two-dimensional area grid Ξ, and arrange all P(x, y) into an M×N cumulative value table Ω according to the indexes of m and n, Ω(m,n)=P(x, y);

[0163] Obtain the maximum value point in the cumulative value table Ω and the position coordinates corresponding to the maximum value point in the two-dimensional area grid Ξ.

[0164] The position coordinates are obtained based on the position coordinates corresponding to the maximum value point in the two-dimensional area grid III.

[0165] In one implementation, the formula for obtaining the position coordinates is:

[0166]

[0167] It can be seen that the present invention improves the performance gain of the positioning system through multi-antenna deep fusion, and adopts the distance search method to determine the position of the drone. A two-dimensional plane coordinate system is established with a single antenna base station as the center origin. By traversing all the assumed positions, a table of cross-correlation power spectrum integral values ​​of the signals received by different antenna base stations can be obtained. Find the maximum value in the table, and the corresponding estimated position can be obtained.

[0168] In summary, compared with the existing multi-station positioning method and system, the implementation method proposed by the present invention can obtain higher positioning accuracy. In addition, the solution does not require the actual sensor element to perform direction finding and delay measurement, and saves hardware costs by using the method of assuming position. And because the arrival angle and time difference information are used at the same time, the performance of the solution in various scenarios can be more adaptable.

[0169] In addition, please refer to Figure 5 The embodiment of the present invention further provides a multi-station positioning system based on TDOA and AOA, including:

[0170] K antenna base stations 61, used to locate the UAV, at least one of the antenna base stations 61 includes two or more receiving antennas, and K is an integer greater than or equal to 2;

[0171] A target signal receiving module 62 is used to use each receiving antenna to receive a signal within its measurement range respectively, and if a target signal is received, generate first target data, wherein the first target data includes time domain information of the target signal;

[0172] A coordinate system establishing module 63 is used to establish a two-dimensional plane coordinate system based on all the first target data, and obtain the coordinates of each antenna base station 61, and the coordinates of each antenna base station 61 are all coordinates on the two-dimensional plane coordinate system;

[0173] The assumed coordinate acquisition module 64 is used to obtain a number of assumed coordinates corresponding to when the drone sends the target signal based on the range and step size of the two-dimensional plane coordinate system;

[0174] A cross-correlation power spectrum cumulative value acquisition module 65, configured to obtain a cross-correlation power spectrum cumulative value corresponding to each assumed coordinate based on the first target data and the coordinates of each antenna base station 61;

[0175] The UAV position determination module 66 is used to determine the position coordinates of the UAV based on the accumulated value of the cross-correlation power spectrum corresponding to each assumed coordinate;

[0176] The assumed coordinates corresponding to the maximum cross-correlation power spectrum accumulation value are the position coordinates.

[0177] In one implementation, please refer to Figure 6 , the cross-correlation power spectrum cumulative value acquisition module 65 includes:

[0178] The wave direction angle and relative distance calculation unit 651 is used to obtain the wave direction angle of the target signal corresponding to the antenna base station 61 and the relative distance of the drone corresponding to each antenna base station 61 based on the assumed coordinates and the coordinates of each antenna base station 61;

[0179] A first processing unit 652 is used to perform a first processing on all the first target data corresponding to each antenna base station 61 based on the arrival direction angle of the target signal corresponding to each antenna base station 61 to generate corresponding second target data, wherein the first processing includes a deep fusion processing and an azimuth compensation processing;

[0180] The relative time difference calculation unit 653 is used to determine the relative time difference between the antenna base stations 61 based on the arrival direction angle of the target signal corresponding to each antenna base station 61 and the relative distance of the UAV corresponding to each antenna base station 61;

[0181] A cross-power spectrum integral absolute value calculation unit 654 is used to obtain a cross-power spectrum integral absolute value between the antenna base stations 61 after time difference compensation based on the relative time difference between the antenna base stations 61 and each of the second target data;

[0182] The cross-correlation power spectrum cumulative value calculation unit 655 is used to accumulate all the cross-power spectrum integral absolute values ​​corresponding to each of the assumed coordinates to generate a cross-correlation power spectrum cumulative value.

[0183] In summary, the embodiment of the present invention uses K antenna base stations to locate the UAV, at least one antenna base station includes two or more receiving antennas, and a two-dimensional plane coordinate system is established through the first target data generated by each antenna when receiving the target signal, and the coordinates of each antenna base station are obtained; based on the range and step size of the two-dimensional plane coordinate system, a number of assumed coordinates corresponding to the target signal sent by the UAV are obtained; based on the first target data and the coordinates of each antenna base station, the cumulative value of the cross-correlation power spectrum corresponding to each assumed coordinate is obtained; based on the cumulative value of the cross-correlation power spectrum corresponding to each assumed coordinate, the position coordinates of the UAV are determined. The present invention searches for the position of the UAV by establishing a coordinate system and a number of assumed coordinates, and the positioning accuracy obtained is higher, and no actual sensor elements are required, so that the present invention has a lower hardware cost and a simpler industrial implementation.

[0184] Although the present invention is disclosed as above, the present invention is not limited thereto. Any person skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the scope defined by the claims.

Claims

1. A multi-station positioning method based on TDOA and AOA, characterized in that: Positioning the UAV using K antenna base stations, at least one of the antenna base stations includes two or more receiving antennas, and K is an integer greater than or equal to 2; The multi-station positioning method includes: Each receiving antenna receives a signal within its measurement range respectively, and if a target signal is received, generates first target data, wherein the first target data includes time domain information of the target signal; Based on all the first target data, a two-dimensional plane coordinate system is established, and coordinates of each antenna base station are obtained, where the coordinates of each antenna base station are coordinates on the two-dimensional plane coordinate system; Based on the range and step size of the two-dimensional plane coordinate system, a number of assumed coordinates corresponding to when the UAV sends the target signal are obtained; Based on the first target data and the coordinates of each antenna base station, obtaining a cross-correlation power spectrum accumulation value corresponding to each assumed coordinate; Determine the position coordinates of the UAV based on the accumulated value of the cross-correlation power spectrum corresponding to each assumed coordinate; The assumed coordinates corresponding to the maximum cross-correlation power spectrum accumulation value are the position coordinates.

2. The multi-station positioning method according to claim 1, characterized in that: Obtaining the accumulated value of the cross-correlation power spectrum corresponding to the assumed coordinates, comprising: Based on the assumed coordinates and the coordinates of each antenna base station, obtaining the incoming wave direction angle of the target signal corresponding to the antenna base station and the relative distance of the UAV corresponding to each antenna base station; Based on the arrival direction angle of the target signal corresponding to each antenna base station, performing a first processing on all the first target data corresponding to each antenna base station to generate corresponding second target data, wherein the first processing includes a deep fusion processing and an azimuth compensation processing; Based on the incoming wave direction angle of the target signal corresponding to each antenna base station and the relative distance of the UAV corresponding to each antenna base station, determine the relative time difference between the antenna base stations; Based on the relative time difference between the antenna base stations and each of the second target data, obtaining an absolute value of a cross-power spectrum integral between the antenna base stations after time difference compensation; All the absolute values ​​of the cross-power spectrum integrals corresponding to each of the assumed coordinates are accumulated to generate a cross-correlation power spectrum accumulation value.

3. The multi-station positioning method according to claim 2, characterized in that: The range of the two-dimensional plane coordinate system includes the range of the first direction x∈[x low ,x high ] and the step length x in the first direction step , and the range of the second direction y∈[y low ,y high ] and the step length y in the second direction step , and the first direction is perpendicular to the second direction; Based on the range and step size of the two-dimensional plane coordinate system, several assumed coordinates corresponding to when the drone sends the target signal are obtained, including: Based on the range and step size of the two-dimensional plane coordinate system, determine the M×N two-dimensional regional network Ξ, where M=(x high -x low ) / x step +1, N = (y high -y low ) / y step +1; Based on all grid cells in the two-dimensional area network, the assumed coordinates corresponding to each grid cell are determined.

4. The multi-station positioning method according to claim 3, characterized in that: Based on the grid unit Ξ(m,n) in the two-dimensional area network, determining the assumed coordinates (x,y) corresponding thereto, comprising: Determine the assumed coordinates (x, y) corresponding to the grid unit Ξ(m, n) based on the step size corresponding to the grid unit Ξ(m, n), and Here, m and n are both integers, and 0≤m≤M-1, 0≤n≤N-1.

5. The multi-station positioning method according to claim 4, characterized in that: The antenna base station S with index number k k The first target data corresponding to the pth receiving antenna: Among them, s(t) is the time domain signal transmitted by the UAV, h k For drones and antenna base stations S k The channel gain on the path, τ k represents the delay caused by the path, Represents the antenna base station S k The signal direction angle function of the p-th receiving antenna, and 0≤k≤K-1, p≥1.

6. The multi-station positioning method according to claim 5, characterized in that: Antenna base station S k Second target data Among them, θ k For the drone at the antenna base station S k The incoming wave direction angle, and θ k =arctan(x k ,yy k ); l k The UAV corresponds to the antenna base station S k The relative distance of N k is the antenna base station S k The total number of receiving antennas.

7. The multi-station positioning method according to claim 6, characterized in that: The K antenna base stations form an equally spaced circular antenna array.

8. The multi-station positioning method according to claim 7, characterized in that: Antenna base station S k The wave direction angle function in, R k is the antenna base station S k The radius of the antenna array, is the antenna base station S k is the position angle of the pth receiving antenna, and λ represents the wavelength of the carrier.

9. The multi-station positioning method according to claim 6, characterized in that: The antenna base station S with index number k k The antenna base station S with index number i i The relative time difference between Among them, i≠k, and 0≤i≤K-1.

10. The multi-station positioning method according to claim 6, characterized in that: Antenna base station S k With antenna base station S i The absolute value of the cross power spectrum integral after time difference compensation Where, s(w) is the target signal in the frequency domain, is the antenna base station S k and antenna base station S i Phase deviation caused by clock asynchrony, is a constant.

11. A multi-station positioning system based on TDOA and AOA, characterized in that: include: K antenna base stations, used to locate the UAV, at least one of the antenna base stations includes two or more receiving antennas, and K is an integer greater than or equal to 2; A target signal receiving module, used to use each receiving antenna to receive a signal within its measurement range respectively, and if a target signal is received, generate first target data, wherein the first target data includes time domain information of the target signal; A coordinate system establishment module, used to establish a two-dimensional plane coordinate system based on all the first target data, and obtain the coordinates of each antenna base station, where the coordinates of each antenna base station are all coordinates on the two-dimensional plane coordinate system; An assumed coordinate acquisition module, used for obtaining a number of assumed coordinates corresponding to when the UAV sends the target signal based on the range and step size of the two-dimensional plane coordinate system; A cross-correlation power spectrum cumulative value acquisition module, used to obtain a cross-correlation power spectrum cumulative value corresponding to each assumed coordinate based on the first target data and the coordinates of each antenna base station; A drone position determination module, used to determine the position coordinates of the drone based on the accumulated value of the cross-correlation power spectrum corresponding to each assumed coordinate; The assumed coordinates corresponding to the maximum cross-correlation power spectrum accumulation value are the position coordinates.

12. The multi-station positioning system according to claim 11, characterized in that: The cross-correlation power spectrum cumulative value acquisition module includes: An incoming wave direction angle and relative distance calculation unit is used to obtain the incoming wave direction angle of the target signal corresponding to the antenna base station and the relative distance of the UAV corresponding to each antenna base station based on the assumed coordinates and the coordinates of each antenna base station; A first processing unit, configured to perform a first processing on all the first target data corresponding to each antenna base station based on the arrival direction angle of the target signal corresponding to each antenna base station to generate corresponding second target data, wherein the first processing includes a deep fusion processing and an azimuth compensation processing; A relative time difference calculation unit, used to determine the relative time difference between the antenna base stations based on the incoming wave direction angle of the target signal corresponding to each antenna base station and the relative distance of the UAV corresponding to each antenna base station; A cross-power spectrum integral absolute value calculation unit, used to obtain a cross-power spectrum integral absolute value between the antenna base stations after time difference compensation based on the relative time difference between the antenna base stations and each of the second target data; The cross-correlation power spectrum cumulative value calculation unit is used to accumulate all the cross-power spectrum integral absolute values ​​corresponding to each of the assumed coordinates to generate a cross-correlation power spectrum cumulative value.

Images