A multi-base station cooperative unmanned aerial vehicle perception method and system based on matched filtering

Abstract

This invention belongs to the field of wireless communication and discloses a multi-base station cooperative UAV perception method and system based on matched filtering. The method includes: determining the target area to be perceived by the UAV; constructing a compensation matrix for each base station based on the echo signal of each base station; performing matched filtering on the compensation matrix and the observation signal matrix to output single-base station parameter estimation results; calculating the estimated position of the UAV at the current base station based on the single-base station parameter estimation results; weighted fusing the position estimates of all base stations to obtain the UAV's three-dimensional spatial position; constructing a system of linear equations based on the base station position, the base station parameter estimation results, and the three-dimensional spatial position; solving the system of linear equations to obtain the UAV's three-dimensional velocity vector; and outputting the UAV perception result containing the three-dimensional spatial position and the three-dimensional velocity vector. The method has low computational complexity and high real-time performance, making it suitable for large-scale deployment of multi-base station cooperative perception systems.

Description

Technical Field

[0001] This invention relates to the field of wireless communication, and in particular to a multi-base station cooperative UAV sensing method and system based on matched filtering. Background Technology

[0002] The rapid development of drone technology has led to its widespread application in many industries. Drone networks have the advantages of flexible deployment, low cost, and wide coverage, and are widely used in logistics, agriculture, environment, public safety, emergency response and other fields, showing an explosive growth trend.

[0003] However, the continuous increase in the number of drones, especially their large-scale deployment in urban low-altitude airspace, has brought unprecedented airspace management challenges. How to effectively regulate dense drone swarms, ensure flight safety, avoid collisions, and guarantee the stability of communication links has become a core issue that urgently needs to be addressed.

[0004] In recent years, Integrated Sensing and Communication (ISAC) has become one of the important technologies for achieving full-domain perception in UAV application scenarios. Under the ISAC architecture, communication signals are used as sensing and detection signals. At the receiving end, the target parameters are extracted by analyzing the echo signals, thereby achieving target detection and localization. However, existing ISAC sensing technologies have significant bottlenecks in both single-base station parameter estimation and multi-base station fusion. The single-base station solution is limited by observability and low SNR performance, while the multi-base station grid search solution has high complexity, poor real-time performance, and high deployment requirements, making it difficult to meet the comprehensive requirements of high precision, low complexity, and strong robustness in future low-altitude high-speed UAV surveillance scenarios. Therefore, existing ISAC technologies cannot simultaneously meet the requirements of high sensing accuracy and low computational complexity for real-time perception of low-altitude UAV swarms.

[0005] Existing technologies still have the problem that ISAC technology cannot simultaneously meet the requirements of high perception accuracy and low computational complexity for real-time perception of low-altitude UAV swarms. Therefore, existing technologies need to be improved. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a multi-base station cooperative UAV perception method and system based on matched filtering, in order to address the shortcomings of the existing technology, and solve the problem that the existing ISAC technology cannot simultaneously meet the requirements of high perception accuracy and low computational complexity for real-time perception of low-altitude UAV swarms.

[0007] The technical solution adopted by this invention to solve the technical problem is as follows:

[0008] In a first aspect, the present invention provides a multi-base station cooperative UAV perception method based on matched filtering, comprising:

[0009] Determine the target area to be sensed by the UAV, acquire the echo signals of all base stations in the target area, and obtain the observation signal matrix based on the echo signals;

[0010] Based on the echo signal of the drone at each base station, a compensation matrix is ​​constructed for the corresponding base station;

[0011] The compensation matrix is ​​matched and filtered with the observation signal matrix to output the single base station parameter estimation results.

[0012] The estimated position of the UAV at the current base station is calculated based on the single base station parameter estimation result. The estimated positions of the UAV at all base stations in the target area are weighted and fused to obtain the three-dimensional spatial position of the UAV.

[0013] A system of linear equations is constructed based on the base station location, the single base station parameter estimation results, and the three-dimensional spatial location. The system of linear equations is solved to obtain the three-dimensional velocity vector of the UAV, and the UAV perception results containing the three-dimensional spatial location and the three-dimensional velocity vector are output.

[0014] In one implementation, acquiring the echo signals of all base stations in the target area includes:

[0015] Acquire the transmission signal transmitted by the base station; wherein the transmission signal contains a transmission symbol;

[0016] The reflected signal received from the UAV by the base station is acquired, and the reflected signal is demodulated.

[0017] The transmitted symbol is removed from the demodulated reflected signal to obtain the base station's echo signal.

[0018] In one implementation, obtaining the observation signal matrix based on the echo signal includes:

[0019] Based on the uniform planar array configured for the base station, obtain the array direction vector of the base station;

[0020] The echo signal is combined with the array direction vector to obtain the observation signal matrix of the base station.

[0021] In one implementation, constructing a compensation matrix for the corresponding base station based on the echo signal of the UAV for each base station includes:

[0022] Based on the echo signal, define the distance range, velocity range, and angle range to be searched;

[0023] The distance interval is quantized into a first number of candidate distance points, and a distance compensation matrix is ​​constructed based on the candidate distance points;

[0024] The speed range is quantized into a second number of candidate speed points, and a speed compensation matrix is ​​constructed based on the candidate speed points;

[0025] The angle interval is quantized into a third number of azimuth angle discrete values ​​and a fourth number of pitch angle discrete values, and an angle compensation matrix is ​​constructed based on the azimuth angle discrete values ​​and the pitch angle discrete values.

[0026] In one implementation, the step of performing matched filtering on the compensation matrix and the observed signal matrix to output single-base station parameter estimation results includes:

[0027] The constructed distance compensation matrix, velocity compensation matrix, and angle compensation matrix are applied to the observed signal matrix to obtain the parameter estimation matrix;

[0028] Search for the element with the largest modulus in the parameter estimation matrix;

[0029] The row and column indices corresponding to the element with the largest modulus are mapped to distance, velocity, and angle indices to obtain the single base station parameter estimation results; wherein, the single base station parameter estimation results include distance estimates, velocity estimates, azimuth estimates, and elevation estimates.

[0030] In one implementation, the step of calculating the estimated position of the UAV at the current base station based on the single base station parameter estimation result, and then weighted and fused the estimated positions of the UAV at all base stations in the target area to obtain the three-dimensional spatial position of the UAV, includes:

[0031] Based on the current base station location, the distance estimate, the azimuth estimate, and the pitch estimate, calculate the estimated position of the UAV at the current base station;

[0032] The weight of each base station in the target area is calculated based on the distance estimate, and the position estimates of the UAV at all base stations are weighted and fused to obtain the three-dimensional spatial position of the UAV.

[0033] In one implementation, the step of constructing a system of linear equations based on the base station location, the single base station parameter estimation results, and the three-dimensional spatial location, and solving the system of linear equations to obtain the three-dimensional velocity vector of the UAV, includes:

[0034] Construct a unit direction vector based on the base station location and the three-dimensional spatial location;

[0035] Construct a system of linear equations based on the velocity estimate and the unit direction vector;

[0036] Solving the linear equations yields the three-dimensional velocity vector of the UAV.

[0037] Secondly, the present invention provides a multi-base station cooperative UAV perception system based on matched filtering, comprising:

[0038] The sensing signal acquisition module is used to determine the target area to be sensed by the UAV, acquire the echo signals of all base stations in the target area, and obtain the observation signal matrix based on the echo signals;

[0039] The compensation matrix construction module is used to construct a compensation matrix for the corresponding base station based on the echo signal of the UAV for each base station.

[0040] The parameter estimation module is used to perform matched filtering on the compensation matrix and the observation signal matrix, and output the single base station parameter estimation result;

[0041] The three-dimensional position acquisition module is used to calculate the estimated position of the UAV at the current base station based on the single base station parameter estimation result, and to weight and fuse the estimated positions of the UAV at all base stations in the target area to obtain the three-dimensional spatial position of the UAV.

[0042] The velocity vector acquisition module is used to construct a system of linear equations based on the base station location, the single base station parameter estimation results, and the three-dimensional spatial location, and solve the system of linear equations to obtain the three-dimensional velocity vector of the UAV.

[0043] The result output module is used to output the UAV perception results, which include the three-dimensional spatial position and the three-dimensional velocity vector.

[0044] Thirdly, the present invention provides a terminal, comprising: a processor and a memory, wherein the memory stores a multi-base station cooperative UAV perception program based on matched filtering, and the multi-base station cooperative UAV perception program based on matched filtering is executed by the processor to implement the operation of the multi-base station cooperative UAV perception method based on matched filtering as described in the first aspect.

[0045] Fourthly, the present invention also provides a computer-readable storage medium storing a multi-base station cooperative UAV perception program based on matched filtering, wherein the multi-base station cooperative UAV perception program based on matched filtering, when executed by a processor, is used to implement the operation of the multi-base station cooperative UAV perception method based on matched filtering as described in the first aspect.

[0046] The present invention, by employing the above technical solution, has the following effects:

[0047] This invention constructs a compensation matrix based on the echo signal, and then performs matched filtering on the compensation matrix and the observed signal matrix to obtain the output parameter estimation results. This eliminates the need for eigenvalue decomposition, noise subspace construction, and multidimensional peak search, achieving both low complexity and fast, robust parameter extraction. By weighted fusion of the individual base station parameter estimation results from multiple base stations, the final three-dimensional spatial position of the UAV can be obtained. A system of linear equations is then constructed based on the individual base station parameter estimation results and the three-dimensional spatial position, allowing direct solution of the UAV's three-dimensional velocity vector without the need for grid search methods and maximum likelihood estimation. This invention's multi-base station cooperative UAV perception method based on matched filtering offers advantages such as low computational complexity, high scalability, and high real-time performance, making it suitable for large-scale deployment of multi-base station cooperative perception systems. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0049] Figure 1 This is a flowchart of the multi-base station cooperative UAV perception method based on matched filtering in this invention.

[0050] Figure 2 This is a schematic diagram of the structure of a multi-base station cooperative UAV perception system based on ISAC in one implementation of the present invention.

[0051] Figure 3 This is a functional schematic diagram of the terminal in one implementation of the present invention.

[0052] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this invention clearer and more explicit, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0054] Exemplary methods

[0055] In recent years, Integrated Sensing and Communication (ISAC) has become a crucial technology for achieving full-domain perception in UAV applications. Under the ISAC architecture, communication signals are used as sensing and detection signals. At the receiving end, the echo signals are analyzed to extract various target parameters, thereby achieving target detection and localization. However, existing ISAC sensing technologies face significant bottlenecks in both single-base station parameter estimation and multi-base station fusion. Single-base station parameter estimation methods are limited by observability and low SNR performance, relying on eigenvalue decomposition and multi-dimensional peak search, resulting in high computational complexity and poor real-time performance. Furthermore, due to the single observation angle, observability is limited, leading to a significant decrease in localization accuracy when the target is in an edge region or an obstructed scenario. Multi-base station fusion schemes, on the other hand, suffer from high complexity, poor real-time performance, and stringent deployment requirements, making it difficult to meet the comprehensive requirements of high precision, low complexity, and strong robustness in future low-altitude, high-speed UAV surveillance scenarios. Moreover, multi-base station collaborative methods often employ three-dimensional mesh search, with computational load increasing dramatically with search range and resolution, making real-time fusion difficult. They are also susceptible to synchronization errors between base stations, frequency offset, and channel differences, resulting in insufficient stability and engineering usability. Therefore, the existing ISAC technology cannot simultaneously meet the requirements of high perception accuracy and low computational complexity for real-time perception of low-altitude UAV swarms.

[0056] To address the above technical problems, this invention provides a multi-base station cooperative UAV perception method based on matched filtering, comprising: determining a target area to be perceived by the UAV; acquiring echo signals from all base stations in the target area; obtaining an observation signal matrix based on the echo signals; constructing a compensation matrix for the corresponding base station based on the UAV echo signal of each base station; performing matched filtering on the compensation matrix and the observation signal matrix to output a single base station parameter estimation result; calculating the estimated position of the UAV at the current base station based on the single base station parameter estimation result; weighted fusing the estimated positions of the UAV at all base stations in the target area to obtain the three-dimensional spatial position of the UAV; constructing a system of linear equations based on the base station position, the single base station parameter estimation result, and the three-dimensional spatial position; solving the system of linear equations to obtain the three-dimensional velocity vector of the UAV; and outputting a UAV perception result containing the three-dimensional spatial position and the three-dimensional velocity vector. This method has low computational complexity and high real-time performance, making it suitable for large-scale deployment of multi-base station cooperative perception systems.

[0057] like Figure 1 As shown, this embodiment of the invention provides a multi-base station cooperative UAV perception method based on matched filtering, including the following steps:

[0058] Step S100: Determine the target area to be perceived by the UAV, acquire the echo signals of all base stations in the target area, and obtain the observation signal matrix based on the echo signals.

[0059] Specifically, in one implementation of this embodiment, step S100 includes the following steps:

[0060] Step S101: Determine the target area to be sensed by the UAV.

[0061] It should be noted that the target area to be sensed by the drone can be regarded as a multi-base station cooperative drone sensing system based on ISAC, such as... Figure 2 The diagram shown is a schematic representation of a multi-base station cooperative UAV perception system based on ISAC in one implementation of this embodiment. Figure 2 It includes a drone; four base stations: base station 1, base station 2, base station 3, and base station 4 (the number of base stations is not limited to four; the diagram is for reference only); and a fusion center; blue dashed lines represent transmitted signals; red dashed lines represent echo signals; and green solid lines represent data transmitted from the base stations to the fusion center for fusion. This multi-base station collaborative drone sensing system includes... Each base station is equipped with a Uniform Planar Array (UPA) for collaborative sensing of the same drone.

[0062] In a multi-base station cooperative UAV sensing system based on ISAC, the UAV's position and velocity are denoted as follows: and , No. Location of each base station In this system, each base station transmits ISAC signals based on Orthogonal Frequency Division Multiplexing (OFDM) and receives reflected signals from the UAV. Subsequently, each base station independently performs a preprocessing procedure to determine the UAV's radial velocity. radial distance and the azimuth angle relative to the base station and pitch angle A preliminary estimate was made, among which . No. The estimate obtained from each base station is denoted as . The data is then transmitted to the fusion center via wired backhaul links, where it is processed by the fusion center. The measurement results from each base station are comprehensively processed to obtain the final position estimate of the UAV. and speed estimation .

[0063] In this embodiment, the uniform planar array of each base station has ideal full-duplex operation capability, and mutual interference between different base stations is avoided through resource orthogonality or spatial separation; the channel is regarded as mainly propagating at line of sight (LoS), and multipath effects are ignored; the UAV is modeled as a point target with constant radar cross-section (RCS) and maintaining uniform motion within a single sensing frame.

[0064] Step S102: Obtain the transmission signal transmitted by the base station; wherein the transmission signal contains a transmission symbol.

[0065] In this embodiment, each base station transmits signals based on orthogonal frequency division multiplexing waveforms, and each sensing frame is defined to contain... 1 OFDM symbol, each symbol containing 1 OFDM symbol. Subcarrier. With the first Taking a base station as an example, its transmitted signal can be represented as a complex exponential form with multiple subcarriers and multiple symbols superimposed. The subcarrier spacing, carrier frequency, and initial phase are all inherent parameters of the communication system. The specific transmitted signal model is as follows:

[0066] ;

[0067] in, The transmitted symbol is digitally modulated, derived from a Quadrature Phase Shift Keying (QPSK) constellation, and satisfies... ; For carrier frequency; For subcarrier spacing, Indicates the initial phase; The duration of a single OFDM symbol, This refers to the moment when the first symbol begins to be emitted. Additionally, the rectangular window function... The definition of is: The value is 1 if the condition is met, and 0 otherwise.

[0068] In this embodiment, the above-mentioned transmission signal model enables the system's transmission signal to possess the orthogonality and bandwidth utilization of standard OFDM waveforms, thereby ensuring the normal operation of communication services and taking into account the time-frequency resolution capability required for subsequent sensing processing.

[0069] Step S103: Obtain the reflected signal from the UAV received by the base station, and demodulate the reflected signal.

[0070] In this embodiment, the UAV generates a reflected signal from the OFDM signal transmitted by the base station. This reflected signal is subject to various physical influences related to the target's motion state and spatial position during propagation. For the first... The reflected signal received by a base station can be abstractly described as a combination of the transmitted signal's delay, Doppler frequency shift, and amplitude attenuation along the propagation path:

[0071] ;

[0072] in, This represents the amplitude attenuation coefficient of the signal along its propagation path; For the first The propagation distance between each base station and the target; This indicates the radial velocity of the drone along the line-of-sight direction of the base station; Represents the speed of light constant. Noise term. It is additive Gaussian noise with a mean of zero and a variance of . The model shows that the target distance causes a time delay in the echo signal, while the target velocity is reflected in the time change of the signal phase through the Doppler effect. The two correspond to structured phase changes in the frequency domain and symbol time direction, respectively, under OFDM waveform, which are the main basis for subsequent distance and velocity estimation.

[0073] In this embodiment, after the echo signal is received, the first Each base station uses an FFT (fast Fourier transform) module consistent with the communication system to perform OFDM demodulation on the echo signal, obtaining the complex echo quantity corresponding to each subcarrier and each symbol.

[0074] Step S104: Remove the transmitted symbol from the demodulated reflected signal to obtain the echo signal of the base station.

[0075] In this embodiment, to eliminate the impact of communication modulation on sensing processing, the modulated transmission symbols in the OFDM system are obtained. The demodulated echo symbol is divided by the corresponding transmitted symbol to obtain the equivalent echo signal containing only target range and radial velocity information. The processed echo signal on each subcarrier and OFDM symbol can be represented as follows:

[0076] ;

[0077] In this embodiment, based on the model of the echo signal, the inherent time-frequency structure of the OFDM signal can be used in the reflected signal receiving and processing stage to reflect the target's distance and velocity characteristics in different dimensions in a separable manner, thereby significantly improving the observability and robustness of parameter estimation.

[0078] Step S105: Obtain the array direction vector of the base station based on the uniform planar array configured by the base station.

[0079] In this embodiment, to obtain the spatial angle information of the UAV, each base station is configured with a... In a uniform planar array composed of 1000 array elements, due to the spatial differences in the different antenna elements within the array, the echo signal generates a deterministic phase difference between the elements, determined by the azimuth and elevation angles. The corresponding array direction vector can be expressed as:

[0080] ;

[0081] in, is the array direction vector.

[0082] In this embodiment, the phase distribution of the target incident direction in the array spatial dimension is characterized by the direction vector, so that the array can extract azimuth and elevation information from the spatial characteristics of the echo.

[0083] Step S106: Combine the echo signal with the array direction vector to obtain the observation signal matrix of the base station.

[0084] In this embodiment, based on the echo signal and array direction vector, the echo matrix of each subcarrier is combined with the array response to obtain the base station's observation signal matrix. For the first... The complete observation signal matrix of a base station can be represented as:

[0085] ;

[0086] in, It is a two-dimensional symbolic matrix containing distance and velocity features. This indicates the array receiving noise. This represents the Kronecker product. The observation signal matrix simultaneously encodes the target's range, velocity, azimuth, and elevation information.

[0087] Based on the observed signal matrix, a preliminary set of target parameters can be obtained. This data will be used for subsequent multi-base station collaborative fusion processing.

[0088] In one implementation of this embodiment, the method further includes: if the approximate location of the drone is known, but its precise location is unknown, the target area can be determined based on the drone to be sensed. Specifically, the method for determining the target area based on the drone to be sensed includes the following steps:

[0089] Step a: Obtain the reflected signals from the drone received by all base stations;

[0090] Step b: Determine the target area of ​​the UAV to be sensed based on the reflected signal;

[0091] Step c: Obtain the transmission signals transmitted by all base stations within the target area, and demodulate the reflected signals from the UAV received by all base stations within the target area; wherein, the transmission signals contain transmission symbols;

[0092] Step d: Remove the transmitted symbols from the demodulated reflected signal to obtain the echo signal from the base station;

[0093] Step e: Obtain the array direction vector of the base station based on the uniform planar array configured by the base station;

[0094] Step f: Combine the echo signal with the array direction vector to obtain the observation signal matrix of the base station.

[0095] In this embodiment, the target area of ​​the drone to be sensed is determined based on the reflected signal. Specifically, a set of base stations in the target area is established, and it is determined in turn whether each base station receives the reflected signal of the drone to be sensed. If the current base station receives the reflected signal of the drone to be sensed, then the current base station is added to the set of base stations in the target area.

[0096] like Figure 1 As shown, this embodiment of the invention provides a multi-base station cooperative UAV perception method based on matched filtering, including the following steps:

[0097] Step S200: Based on the echo signal of the UAV at each base station, construct a compensation matrix for the corresponding base station.

[0098] In this embodiment, based on the constructed observation signal model A fast parameter estimation method based on matched filtering features is introduced on the single base station side. This method utilizes the structured phase changes of the echo signal in the frequency domain, time domain and array space dimension, and performs correlation operations with template signals corresponding to different parameter combinations to achieve low-complexity estimation of target distance, radial velocity, azimuth angle and elevation angle.

[0099] In this embodiment, to achieve rapid estimation of the target's radial distance and velocity using the phase structure of the echo signal in the frequency and symbol domains, the first... Each base station constructs a compensation matrix, which includes a distance compensation matrix, a velocity compensation matrix, and an angle compensation matrix.

[0100] Specifically, in one implementation of this embodiment, step S200 includes the following steps:

[0101] Step S201: Define the distance range, velocity range, and angle range to be searched based on the echo signal.

[0102] In this embodiment, the distance range to be searched is defined based on the echo signal. The speed range to be searched is The angle range includes the azimuth range and the elevation range, where the azimuth range is... The pitch angle range is .

[0103] Step S202: Quantize the distance interval into a first number of candidate distance points, and construct a distance compensation matrix based on the candidate distance points.

[0104] In this embodiment, the first quantity is The distance range to be searched Uniform quantization Candidate distance points Based on this, the dimensions are... Distance compensation matrix :

[0105] .

[0106] It should be noted that, to ensure the unambiguity of matched filter estimation within a single-base station sensing framework, the distance interval to be searched... Set the constraints as follows:

[0107] .

[0108] In this embodiment, a distance compensation matrix is ​​used. Compensation is provided for the phase term introduced by subcarrier frequency shift under different assumed distances.

[0109] Step S203: Quantize the speed range into a second number of candidate speed points, and construct a speed compensation matrix based on the candidate speed points.

[0110] In this embodiment, the second quantity is The speed range to be searched Quantified as Candidate velocity points and construct a size of velocity compensation matrix :

[0111] .

[0112] It should be noted that, to ensure the unambiguity of matched filter estimation within a single-base station sensing framework, the speed range to be searched... Set the constraints as follows:

[0113] .

[0114] In this embodiment, by setting constraints on the distance range and velocity range to be searched, it is ensured that the matched filter output has only a single main peak in the distance and velocity dimensions, avoiding estimation ambiguity caused by phase periodicity, thereby improving the reliability of parameter estimation.

[0115] In this embodiment, a velocity compensation matrix is ​​used. Compensation is provided for the inter-symbol phase changes introduced by the Doppler effect under different assumed velocities.

[0116] Step S204: Quantize the angle interval into a third number of azimuth discrete values ​​and a fourth number of pitch discrete values, and construct an angle compensation matrix based on the azimuth discrete values ​​and pitch discrete values.

[0117] In this embodiment, the third quantity is The fourth quantity is azimuth interval Evenly divided into discrete values pitch angle range Evenly divided into discrete values Thus, the total is obtained. Discrete angle pair For each set of candidate angles, the corresponding guiding vector is calculated based on the array geometry. and arrange their Hermetic conjugates into a size of Angle compensation matrix :

[0118] .

[0119] like Figure 1 As shown, this embodiment of the invention provides a multi-base station cooperative UAV perception method based on matched filtering, including the following steps:

[0120] Step S300: Perform matched filtering on the compensation matrix and the observation signal matrix to output the single base station parameter estimation result.

[0121] Specifically, in one implementation of this embodiment, step S300 includes the following steps:

[0122] Step S301: Apply the constructed distance compensation matrix, velocity compensation matrix, and angle compensation matrix to the observed signal matrix to obtain the parameter estimation matrix.

[0123] In this embodiment, in order to construct without explicit configuration Under the premise of achieving joint matched filtering estimation of distance, velocity, and angle, this invention utilizes the Kronecker product operation to combine the distance compensation matrix and the angle compensation matrix, and applies them together with the velocity compensation matrix to the complete observation signal matrix. The parameter estimation matrix is ​​obtained. The corresponding formula is as follows:

[0124] ;

[0125] Among them, record The Middle The elements are .

[0126] Step S302: Search for the element with the largest modulus in the parameter estimation matrix.

[0127] In this embodiment, in the parameter estimation matrix The element with the largest modulus in the search is:

[0128] ;

[0129] in, That is, the parameter estimation matrix The element with the largest modulus value.

[0130] Step S303: Map the row and column indices corresponding to the element with the largest modulus value to the distance index, velocity index, and angle index to obtain the single base station parameter estimation results; wherein, the single base station parameter estimation results include distance estimates, velocity estimates, azimuth estimates, and elevation estimates.

[0131] In this embodiment, the element with the largest modulus value is... The corresponding row and column indices are mapped to distance, velocity, and angle indices, thus obtaining the joint single-base station parameter estimation results. ;in, This is a distance estimate; This is a speed estimate; This is the estimated azimuth angle; This is the estimated pitch angle.

[0132] In this embodiment, the number estimation matrix is ​​used. It enables the simultaneous matching filter estimation of range, velocity, azimuth and pitch angles under a single computational structure, which not only ensures the consistency between parameters, but also avoids the additional overhead caused by separate estimation and multiple searches.

[0133] like Figure 1 As shown, this embodiment of the invention provides a multi-base station cooperative UAV perception method based on matched filtering, including the following steps:

[0134] Step S400: Calculate the estimated position of the UAV at the current base station based on the single base station parameter estimation result, and weight and fuse the estimated positions of the UAV at all base stations in the target area to obtain the three-dimensional spatial position of the UAV.

[0135] In this embodiment, after obtaining the parameter estimation results of each base station independently, a low-complexity spatial fusion method based on weighted geometric averaging is proposed to calculate the three-dimensional spatial position of the UAV. Unlike existing fusion methods that use three-dimensional grid traversal or grid search, this embodiment does not rely on multi-dimensional grid discretization. Instead, it directly utilizes the radial distance and angle information provided by each base station to construct spatial geometric relationships, and then fuses the preliminary position estimates of multiple base stations in a weighted manner, significantly reducing the computational load.

[0136] Specifically, in one implementation of this embodiment, step S400 includes the following steps:

[0137] Step S401: Calculate the estimated position of the UAV at the current base station based on the current base station location, the distance estimate, the azimuth estimate, and the pitch estimate.

[0138] In this embodiment, for any first... For each base station, obtain the distance estimate from its single base station parameter estimation results. Azimuth estimate and pitch angle estimates Combined with base station location The approximate position estimate of the UAV in three-dimensional space was calculated. Its expression is:

[0139] ;

[0140] Rough position estimate of the UAV in three-dimensional space Describes the drone relative to the first The spatial location of each base station along the observation direction.

[0141] Step S402: Calculate the weight of each base station in the target area based on the distance estimate, and weight and fuse the position estimates of the UAV at all base stations to obtain the three-dimensional spatial position of the UAV.

[0142] In this embodiment, the weight of each base station in the target area is calculated based on the distance estimate, and the weight is inversely proportional to the distance estimate.

[0143] In this embodiment, the position estimates of the UAV from all base stations are weighted and fused to obtain the three-dimensional spatial position of the UAV; that is, a coarse position estimate of all base stations. A weighted average is used to obtain the final three-dimensional spatial position, and the corresponding formula is as follows:

[0144] ;

[0145] in, As weight.

[0146] In this embodiment, the three-dimensional spatial position calculation formula based on weighted averaging effectively reduces the impact of measurement errors from long-distance base stations on the final result, thereby improving the robustness of positioning. Compared with traditional three-dimensional positioning methods based on 3D grid search, the weighted geometric average method in this embodiment does not require discretization of the three-dimensional space or matching calculations for a large number of spatial grid points, significantly reducing computational complexity and making it more suitable for real-time sensing scenarios.

[0147] In one implementation of this embodiment, other weighting functions may also be considered, such as using the confidence level obtained by the joint estimation of distance and angle based on the signal-to-noise ratio (SNR), path loss, channel gain, array gain, or the confidence level obtained by the joint estimation of distance and angle as weights, to replace the weights based on the distance estimate.

[0148] like Figure 1 As shown, this embodiment of the invention provides a multi-base station cooperative UAV perception method based on matched filtering, including the following steps:

[0149] Step S500: Construct a system of linear equations based on the base station location, the single base station parameter estimation results, and the three-dimensional spatial location; solve the system of linear equations to obtain the three-dimensional velocity vector of the UAV.

[0150] In this embodiment, after obtaining the three-dimensional spatial position of the UAV, a three-dimensional velocity fusion method is further adopted based on the consistency constraint of radial velocity of multiple base stations.

[0151] Specifically, in one implementation of this embodiment, step S500 includes the following steps:

[0152] Step S501: Construct a unit direction vector based on the base station location and the three-dimensional spatial location.

[0153] In this embodiment, since a single base station can only obtain the projection of the UAV's velocity vector along the line-of-sight direction, i.e., the radial velocity... Therefore, based on the geometric relationship between the location and estimated location of each base station, a unit direction vector is constructed from the UAV to each base station. :

[0154] .

[0155] Step S502: Construct a system of linear equations based on the velocity estimate and the unit direction vector.

[0156] In this embodiment, based on the velocity projection relationship, a system of linear equations is constructed based on the estimated velocity value and the unit direction vector:

[0157] ;

[0158] Among them, matrix The Behavior direction vector ,vector The Each element represents the radial velocity estimate for a single base station. .

[0159] Step S503: Solve the system of linear equations to obtain the three-dimensional velocity vector of the UAV.

[0160] In this embodiment, the least squares method is used to solve the linear equations to obtain an estimate of the UAV's three-dimensional velocity, i.e., the three-dimensional velocity vector.

[0161] The solved three-dimensional velocity vector is as follows:

[0162] .

[0163] In this embodiment, by making full use of the multi-view observation directions of multiple base stations, the three-dimensional velocity can be efficiently recovered without performing a multi-dimensional velocity search.

[0164] This embodiment utilizes a collaborative sensing mechanism formed by position-weighted fusion and velocity linear fusion to achieve joint estimation of the three-dimensional position and three-dimensional velocity of multi-base station UAVs with extremely low computational cost. Since the multi-base station collaborative UAV sensing method based on matched filtering in this embodiment avoids methods such as high-dimensional grid search commonly used in traditional methods, it has advantages such as low computational complexity, strong scalability, and high real-time performance, and is especially suitable for large-scale deployment of multi-base station collaborative sensing systems.

[0165] like Figure 1 As shown in the figure, this embodiment of the invention provides a multi-base station cooperative UAV perception method based on matched filtering, which further includes the following steps:

[0166] Step S600: Output the UAV perception result containing the three-dimensional spatial position and the three-dimensional velocity vector.

[0167] In this embodiment, the three-dimensional spatial position of the UAV obtained by weighted fusion of the location estimates of each base station, and the three-dimensional velocity vector of the UAV obtained by solving the linear equation system composed of the base station position, single parameter estimation results and three-dimensional spatial position, are output as the UAV perception results.

[0168] This embodiment achieves the following technical effects through the above technical solution:

[0169] This paper presents a multi-base station cooperative UAV perception method based on matched filtering. It eliminates the need for eigenvalue decomposition, noise subspace construction, and multi-dimensional spectral peak search. Instead, it employs a low-complexity single-base station parameter estimation algorithm. By constructing range compensation, velocity compensation, and angle compensation matrices, it performs matched filtering operations with the observed signal matrix and directly obtains the estimated results of range, velocity, azimuth, and elevation angles using the maximum amplitude criterion. It fully utilizes the structured phase characteristics of OFDM signals in the frequency, symbol, and spatial domains to achieve fast and robust parameter extraction.

[0170] A lightweight multi-base station location fusion method is used, which does not rely on 3D search or grid traversal. A coarse position is constructed by using the distance and angle information estimated by each base station individually, and then a spatial weighted geometric average is performed with inverse distance squared as the weight to obtain the final 3D spatial position of the UAV.

[0171] By utilizing the relationship between radial velocity estimation from multiple base stations and spatial geometric orientation, a system of linear equations is constructed. The three-dimensional velocity vector of the target is solved in one step using the least squares method, without the need for velocity-dimensional grid search or joint maximum likelihood estimation.

[0172] Therefore, the multi-base station cooperative UAV perception method based on matched filtering provided in this embodiment has advantages such as low computational complexity, strong scalability, and high real-time performance, and is especially suitable for large-scale deployment of multi-base station cooperative perception systems.

[0173] Exemplary device

[0174] Based on the above embodiments, the present invention also provides a multi-base station cooperative UAV perception system based on matched filtering, comprising:

[0175] The sensing signal acquisition module is used to determine the target area to be sensed by the UAV, acquire the echo signals of all base stations in the target area, and obtain the observation signal matrix based on the echo signals;

[0176] The compensation matrix construction module is used to construct a compensation matrix for the corresponding base station based on the echo signal of the UAV for each base station.

[0177] The parameter estimation module is used to perform matched filtering on the compensation matrix and the observation signal matrix, and output the single base station parameter estimation result;

[0178] The three-dimensional position acquisition module is used to calculate the estimated position of the UAV at the current base station based on the single base station parameter estimation result, and to weight and fuse the estimated positions of the UAV at all base stations in the target area to obtain the three-dimensional spatial position of the UAV.

[0179] The velocity vector acquisition module is used to construct a system of linear equations based on the base station location, the single base station parameter estimation results, and the three-dimensional spatial location, and solve the system of linear equations to obtain the three-dimensional velocity vector of the UAV.

[0180] The result output module is used to output the UAV perception results, which include the three-dimensional spatial position and the three-dimensional velocity vector.

[0181] Based on the above embodiments, the present invention also provides a terminal, the principle block diagram of which can be as follows: Figure 3 As shown.

[0182] The terminal includes: a processor, a memory, an interface, a display screen, and a communication module connected via a system bus; wherein, the processor of the terminal provides computing and control capabilities; the memory of the terminal includes a computer-readable storage medium and internal memory; the computer-readable storage medium stores an operating system and computer programs; the internal memory provides an environment for the operation of the operating system and computer programs in the computer-readable storage medium; the interface is used to connect to external devices; the display screen is used to display relevant information; and the communication module is used to communicate with a cloud server or other devices.

[0183] When executed by the processor, this computer program is used to implement the operation of a multi-base station cooperative UAV perception method based on matched filtering.

[0184] It will be understood by those skilled in the art that Figure 3 The schematic diagram shown is merely a partial structural diagram related to the present invention and does not constitute a limitation on the terminal to which the present invention is applied. A specific terminal may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0185] In one embodiment, a terminal is provided, comprising: a processor and a memory, the memory storing a multi-base station cooperative UAV perception program based on matched filtering, the multi-base station cooperative UAV perception program based on matched filtering being executed by the processor to implement the operation of the multi-base station cooperative UAV perception method based on matched filtering as described above.

[0186] In one embodiment, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a multi-base station cooperative UAV perception program based on matched filtering, which, when executed by a processor, is used to implement the operation of the multi-base station cooperative UAV perception method based on matched filtering as described above.

[0187] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, database, or other media used in the embodiments provided by this invention can include both non-volatile and volatile memory.

[0188] In summary, this invention provides a multi-base station cooperative UAV perception method and system based on matched filtering, comprising: determining a target area to be perceived by the UAV; acquiring echo signals from all base stations in the target area; obtaining an observation signal matrix based on the echo signals; constructing a compensation matrix for the corresponding base station based on the UAV echo signals of each base station; performing matched filtering on the compensation matrix and the observation signal matrix to output a single base station parameter estimation result; calculating the estimated position of the UAV at the current base station based on the single base station parameter estimation result; weighted fusing the estimated positions of the UAV at all base stations in the target area to obtain the three-dimensional spatial position of the UAV; constructing a system of linear equations based on the base station position, the single base station parameter estimation result, and the three-dimensional spatial position; solving the system of linear equations to obtain the three-dimensional velocity vector of the UAV; and outputting the UAV perception result including the three-dimensional spatial position and the three-dimensional velocity vector. This method has low computational complexity and high real-time performance, making it suitable for large-scale deployment of multi-base station cooperative perception systems.

[0189] It should be understood that the application of the present invention is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A multi-base station cooperative UAV perception method based on matched filtering, characterized in that, include: Determine the target area to be sensed by the UAV, acquire the echo signals of all base stations in the target area, and obtain the observation signal matrix based on the echo signals; Based on the echo signal of the drone at each base station, a compensation matrix is ​​constructed for the corresponding base station; The compensation matrix is ​​matched and filtered with the observation signal matrix to output the single base station parameter estimation results. The estimated position of the UAV at the current base station is calculated based on the single base station parameter estimation result. The estimated positions of the UAV at all base stations in the target area are weighted and fused to obtain the three-dimensional spatial position of the UAV. A system of linear equations is constructed based on the base station location, the single base station parameter estimation results, and the three-dimensional spatial location. The three-dimensional velocity vector of the UAV is obtained by solving the system of linear equations. The output includes the UAV perception results containing the three-dimensional spatial position and the three-dimensional velocity vector; The step of constructing a compensation matrix for the corresponding base station based on the echo signal of the UAV at each base station includes: Based on the echo signal, define the distance range, velocity range, and angle range to be searched; The distance interval is quantized into a first number of candidate distance points, and a distance compensation matrix is ​​constructed based on the candidate distance points; The speed range is quantized into a second number of candidate speed points, and a speed compensation matrix is ​​constructed based on the candidate speed points; The angle interval is quantized into a third number of azimuth discrete values ​​and a fourth number of pitch discrete values, and an angle compensation matrix is ​​constructed based on the azimuth discrete values ​​and pitch discrete values. The step of performing matched filtering between the compensation matrix and the observed signal matrix to output single-base station parameter estimation results includes: The constructed distance compensation matrix, velocity compensation matrix, and angle compensation matrix are applied to the observed signal matrix to obtain the parameter estimation matrix; Search for the element with the largest modulus in the parameter estimation matrix; The row and column indices corresponding to the element with the largest modulus are mapped to distance, velocity, and angle indices to obtain the single base station parameter estimation results; wherein, the single base station parameter estimation results include distance estimates, velocity estimates, azimuth estimates, and elevation estimates.

2. The multi-base station cooperative UAV perception method based on matched filtering according to claim 1, characterized in that, The step of acquiring the echo signals of all base stations in the target area includes: Acquire the transmission signal transmitted by the base station; wherein the transmission signal contains a transmission symbol; The reflected signal received from the UAV by the base station is acquired, and the reflected signal is demodulated. The transmitted symbol is removed from the demodulated reflected signal to obtain the base station's echo signal.

3. The multi-base station cooperative UAV perception method based on matched filtering according to claim 1, characterized in that, The process of obtaining the observation signal matrix based on the echo signal includes: Based on the uniform planar array configured for the base station, obtain the array direction vector of the base station; The echo signal is combined with the array direction vector to obtain the observation signal matrix of the base station.

4. The multi-base station cooperative UAV perception method based on matched filtering according to claim 1, characterized in that, The step of calculating the estimated position of the UAV at the current base station based on the single base station parameter estimation result, and then weighting and fusing the estimated positions of the UAV at all base stations in the target area to obtain the three-dimensional spatial position of the UAV includes: Based on the current base station location, the distance estimate, the azimuth estimate, and the pitch estimate, calculate the estimated position of the UAV at the current base station; The weight of each base station in the target area is calculated based on the distance estimate, and the position estimates of the UAV at all base stations are weighted and fused to obtain the three-dimensional spatial position of the UAV.

5. The multi-base station cooperative UAV perception method based on matched filtering according to claim 1, characterized in that, The process of constructing a system of linear equations based on the base station location, the single base station parameter estimation results, and the three-dimensional spatial location, and solving the system of linear equations to obtain the three-dimensional velocity vector of the UAV, includes: Construct a unit direction vector based on the base station location and the three-dimensional spatial location; Construct a system of linear equations based on the velocity estimate and the unit direction vector; Solving the linear equations yields the three-dimensional velocity vector of the UAV.

6. A multi-base station cooperative UAV perception system based on matched filtering, used to implement the multi-base station cooperative UAV perception method based on matched filtering as described in any one of claims 1-5, characterized in that, include: The sensing signal acquisition module is used to determine the target area to be sensed by the UAV, acquire the echo signals of all base stations in the target area, and obtain the observation signal matrix based on the echo signals; The compensation matrix construction module is used to construct a compensation matrix for the corresponding base station based on the echo signal of the UAV for each base station. The parameter estimation module is used to perform matched filtering on the compensation matrix and the observation signal matrix, and output the single base station parameter estimation result; The three-dimensional position acquisition module is used to calculate the estimated position of the UAV at the current base station based on the single base station parameter estimation result, and to weight and fuse the estimated positions of the UAV at all base stations in the target area to obtain the three-dimensional spatial position of the UAV. The velocity vector acquisition module is used to construct a system of linear equations based on the base station location, the single base station parameter estimation results, and the three-dimensional spatial location, and solve the system of linear equations to obtain the three-dimensional velocity vector of the UAV. The result output module is used to output the UAV perception results, which include the three-dimensional spatial position and the three-dimensional velocity vector.

7. A terminal, characterized in that, include: The processor and memory, wherein the memory stores a multi-base station cooperative UAV perception program based on matched filtering, and the multi-base station cooperative UAV perception program based on matched filtering is executed by the processor to implement the multi-base station cooperative UAV perception method based on matched filtering as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a multi-base station cooperative UAV perception program based on matched filtering, which, when executed by a processor, is used to implement the multi-base station cooperative UAV perception method based on matched filtering as described in any one of claims 1-5.

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