Super-resolution detection method and system of swarm targets based on spatial spectrum signal analysis

Abstract

The present invention relates to the field of signal processing technology, and specifically, to a super-resolution detection method and system for swarm targets based on spatial spectrum signal analysis. The method provided by the present invention is used to innovatively integrate traditional detection algorithms and super-resolution detection algorithms, and organically combine the advantages of the two. While achieving high-precision super-resolution functions, the computational complexity of the system is reduced through algorithm optimization and efficient use of computing resources. Compared with systems that rely solely on super-resolution methods, the solution technology can better balance resolution and real-time performance, ensuring instant response capabilities in complex scenarios. Secondly, by introducing signal processing technology, the radar's ability to resolve target angles is enhanced. Even in dense target scenarios, the angle information of adjacent targets can be distinguished as much as possible, thereby improving the system's target detection and tracking capabilities.

Description

Technical Field

[0001] The present invention relates to the field of signal processing technology, and in particular to a swarm target super-resolution detection method and system based on spatial spectrum signal analysis. Background Art

[0002] In recent years, with the rapid development of drone technology, drone swarms have been increasingly used in logistics distribution, disaster relief, agricultural inspections, and national defense security. However, the emergence of drone swarms has also brought many challenges, especially in the context of the development of the low-altitude economy, the need for safety monitoring and management of low-altitude airspace has become increasingly prominent. Due to the large number of targets, strong mobility, and dense distribution of drone swarms, the difficulty of monitoring and identifying them has increased significantly.

[0003] As one of the core low-altitude detection methods, radar plays a key role in the detection and positioning of drone swarm targets. However, due to the limited angular resolution, traditional radar systems have difficulty in effectively distinguishing dense targets, especially in complex scenarios where multiple targets interfere with each other. At present, spatial spectrum signal analysis methods are widely used in radar angle estimation due to their high-resolution characteristics. However, traditional spatial spectrum estimation algorithms have a large amount of computation and are difficult to meet the engineering application requirements of low power consumption and high real-time performance. Summary of the invention

[0004] The purpose of the present invention is to provide a swarm target super-resolution detection method and system based on spatial spectrum signal analysis to solve the above-mentioned problems in the prior art.

[0005] The present invention is achieved through the following technical solutions:

[0006] In a first aspect, the present invention provides a swarm target super-resolution detection method based on spatial spectrum signal analysis, comprising:

[0007] The radar transmitter transmits a radio frequency signal to the target area, the receiver receives the radio frequency echo signal reflected by the target, and converts the radio frequency echo signal into an intermediate frequency signal through a superheterodyne receiver;

[0008] Preprocessing operations are performed on several intermediate frequency signals received by the antenna multi-channel and converted by the superheterodyne receiver to obtain preprocessed signals, and consistency calibration is performed on the amplitude and phase errors between channels based on the preprocessed signals;

[0009] Perform digital beam synthesis and target detection to obtain a target point trace set, perform sliding window detection on the target point trace set, and determine the range unit and Doppler unit where the swarm target is located;

[0010] The multi-channel autocorrelation matrix is ​​calculated through the range unit and Doppler unit in the window where the swarm target is located, and the eigenvalue decomposition and noise subspace extraction of the multi-channel autocorrelation matrix are performed to construct the spatial spectrum function, perform spectrum peak search and target angle analysis;

[0011] The airspace targets are traversed and the detection results are summarized and output to complete the super-resolution detection of targets in the radar scanning airspace.

[0012] Preferably, the performing consistency calibration comprises:

[0013]

[0014] In the formula, is the amplitude calibration coefficient of each channel, is the phase calibration coefficient of each channel, are the signals of each channel after amplitude and phase consistency calibration, is the preprocessed signal, is the unit of the imaginary part of an imaginary number.

[0015] Preferably, the performing digital beamforming and target detection comprises:

[0016] The sum channel signals are obtained through digital beam synthesis, and the target detection algorithm is used to perform target detection on the sum channel signals to generate a set of target point traces in the scanning area.

[0017] Preferably, the obtaining of the channel signal by digital beamforming comprises:

[0018]

[0019] In the formula, is the sum channel signal, for dimensional vector, the number of receiving signal channels.

[0020] Preferably, the distance unit and Doppler unit for determining the location of the swarm target include:

[0021] Application Window The distance units are processed by sliding windows. If there is a target in each distance unit in the sliding window, the target set within the coverage of the window is determined as a swarm target and a swarm target set is generated. Otherwise, no processing is performed.

[0022] Preferably, the calculating of the multi-channel autocorrelation matrix comprises:

[0023]

[0024] In the formula, For the The detected target bee colony corresponds to dimensional multi-channel autocorrelation matrix, For the sake of expectation, To find the conjugate transpose, In the signal matrix after channel calibration, the row number set is Column number is Channel signal, is a constant.

[0025] Preferably, the performing eigenvalue decomposition and noise subspace extraction includes:

[0026]

[0027] In the formula, Indicates The signal subspace of the target, represents the noise subspace, the diagonal matrix and They represent the eigenvalues ​​of the corresponding signal and the eigenvalues ​​of the noise respectively.

[0028] Preferably, the constructing of a spatial spectrum function, performing spectrum peak search and target angle analysis comprises:

[0029]

[0030] In the formula, is the spatial spectrum function, for Find the conjugate transpose of a dimensional vector;

[0031] Preferably, the airspace target traversal and the detection result aggregation and output include:

[0032] The spatial spectrum function is searched for spectral peaks to determine the spectral peak positions. Each spectral peak position corresponds to several targets with different wave directions in the same range gate and Doppler gate. A set consisting of the target is generated until all targets in the swarm target set are traversed and the process ends.

[0033] In a second aspect, the present invention further provides a swarm target super-resolution detection system based on spatial spectrum signal analysis, comprising:

[0034] The signal processing module is configured to transmit a radio frequency signal to a target area through a radar transmitter, receive a radio frequency echo signal reflected by the target through a receiver, and convert the radio frequency echo signal into an intermediate frequency signal through a superheterodyne receiver; perform preprocessing operations on the antenna multi-channel receiving signals respectively to obtain processed signals, and perform consistency calibration on the multi-channel signals and the amplitude and phase errors between channels based on the intermediate frequency signals;

[0035] The detection module is configured to perform digital beam synthesis and target detection, obtain a target point trace set, perform sliding window detection on the target point trace set, and determine the range unit and Doppler unit where the swarm target is located; calculate the multi-channel autocorrelation matrix through the range unit and Doppler unit in the window where the swarm target is located, perform eigenvalue decomposition and noise subspace extraction on the multi-channel autocorrelation matrix, construct a spatial spectrum function, and perform spectrum peak search and target angle analysis;

[0036] The output module is configured to traverse the airspace targets and summarize the detection results and output them, completing the super-resolution detection of targets in the radar scanning airspace.

[0037] The technical solution of the present invention has at least the following advantages and beneficial effects:

[0038] 1. Combining traditional detection algorithms with super-resolution detection algorithms can significantly reduce computational complexity and improve real-time performance.

[0039] This solution innovatively integrates traditional detection algorithms with super-resolution detection algorithms, combining the advantages of both. While achieving high-precision super-resolution functions, the computational complexity of the system is greatly reduced through algorithm optimization and efficient use of computing resources. Compared with systems that rely solely on super-resolution methods, this solution technology can better balance resolution and real-time performance, ensuring instant response capabilities in complex scenarios.

[0040] 2. Break through the angular resolution bottleneck of traditional radar algorithms.

[0041] This solution proposes an effective solution to the problem that the angular resolution of traditional radar algorithms is limited by the beam width within the same range gate and the same Doppler gate. By introducing advanced signal processing technology, the radar's ability to resolve target angles is significantly enhanced. Even in dense target scenes, the angle information of adjacent targets can be clearly distinguished, thereby greatly improving the system's target detection and tracking capabilities.

[0042] 3. Make full use of the spatial spectrum characteristics of multi-channel signals to further improve the angular measurement resolution.

[0043] This scheme fully exploits the spatial spectrum characteristics of multi-channel radar signals and combines efficient array signal processing methods to achieve high-precision estimation of target angles. Compared with traditional single-channel or low-resolution methods, this scheme significantly improves the angular resolution, enabling radar to more accurately identify and locate multiple targets in complex scenes, further expanding the application range of radar systems.

[0044] 4. Support efficient resolution and accurate detection in multi-target scenarios.

[0045] By optimizing the signal processing flow in a multi-target environment, this solution can quickly identify the precise positions and angles of multiple adjacent targets, avoiding the target merging problem caused by insufficient angular resolution in traditional algorithms. This capability has significant advantages in processing complex swarm targets, drone clusters and other high-density multi-target dynamic scenes. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0047] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0049] The division of modules in this application is a logical division. There may be other division methods in actual application. For example, multiple modules can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the connection, coupling or communication in this application can be a direct connection, coupling or communication between related objects, or an indirect connection, coupling or communication through other devices. In addition, the connection, coupling or communication between objects can be electrical or other similar forms, which are not limited in this application.

[0050] The modules or submodules described independently may be physically separated or not: they may be implemented by software or hardware, and some modules or submodules may be implemented by software, and the processor may call the software to implement the functions of these modules or submodules, and other modules or submodules may be implemented by hardware, such as by hardware circuits. In addition, some or all of the modules may be selected according to actual needs to achieve the purpose of the present application.

[0051] Please refer to Figure 1 The present invention provides a swarm target super-resolution detection method based on spatial spectrum signal analysis, comprising:

[0052] S101: transmitting a radio frequency signal to a target area through a radar transmitter, receiving a radio frequency echo signal reflected by the target by a receiver, and converting the radio frequency echo signal into an intermediate frequency signal through a superheterodyne receiver;

[0053] Specifically, signal transmission, echo reception and preprocessing: the radar transmitter transmits RF signals to the target area, the receiver receives the RF echo signals reflected by the target, and down-converts them into intermediate frequency signals through a superheterodyne receiver. The antenna multi-channel received signals are pre-processed by analog-to-digital sampling (AD), digital down-conversion, pulse compression, moving target indication (MTI) and moving target detection (MTD) to obtain the processed signals. .

[0054] in , Indicates the number of pulses. Indicates the number of sampling points, Indicates the receiving signal channel.

[0055] The digital-to-analog sampling (AD), digital down-conversion, pulse compression, moving target indication (MTI) and moving target detection (MTD) are all common radar signal processing operations.

[0056] S102: performing preprocessing operations on a plurality of intermediate frequency signals received by the antenna multi-channel and frequency-converted by the superheterodyne receiver to obtain preprocessed signals, and performing consistency calibration on the amplitude and phase errors between the channels based on the preprocessed signals;

[0057] S103: Perform digital beam synthesis and target detection to obtain a target point trace set, perform sliding window detection on the target point trace set, and determine the range unit and Doppler unit where the swarm target is located;

[0058] S104: Calculate the multi-channel autocorrelation matrix through the range unit and Doppler unit in the window where the swarm target is located, perform eigenvalue decomposition and noise subspace extraction on the multi-channel autocorrelation matrix, construct a spatial spectrum function, perform spectrum peak search and target angle analysis;

[0059] S105: Traverse the airspace targets and summarize and output the detection results to complete the super-resolution detection of targets in the radar scanning airspace.

[0060] This method not only finds an ideal balance between algorithm accuracy and system efficiency, but also achieves significant improvements in key indicators such as radar angular resolution and multi-target detection performance, opening up a new direction for the development of radar technology and having broad application prospects and technical value. By reducing the computational complexity while improving the angular resolution, the feasibility of the direction-finding method based on spatial spectrum signal analysis is ensured, providing an efficient and reliable means of monitoring and positioning drone swarm targets for the development of low-altitude economy, with significant social and economic value and broad application prospects.

[0061] In an exemplary embodiment of the present invention, performing consistency calibration includes:

[0062]

[0063] In the formula, is the amplitude calibration coefficient of each channel, is the phase calibration coefficient of each channel, are the signals of each channel after amplitude and phase consistency calibration, is the preprocessed signal, is the imaginary unit of the imaginary number.

[0064] In an exemplary embodiment of the present invention, the digital beamforming and target detection comprises:

[0065] The sum channel signals are obtained through digital beam synthesis, and the target detection algorithm is used to detect the sum channel signals to generate a set of target points in the scanning area. ;

[0066] ,in, Indicates the total number of targets detected. , Respectively represent the first detection point and the The range gate number and Doppler gate number corresponding to each detection point trace.

[0067] Among them, the target detection algorithm includes but is not limited to the unit average horizontal false alarm algorithm (CA-CFAR), the unit large horizontal false alarm algorithm (GO-CFAR), the unit small horizontal false alarm algorithm (SO-CFAR), etc. The present invention selects but is not limited to the Doppler large-distance unit average horizontal false alarm algorithm, that is, first select the maximum amplitude signal in the Doppler channel (pulse number dimension), and then use the unit average horizontal false alarm algorithm in the distance dimension (sampling point number dimension) detection to perform target detection operation. This algorithm is a general algorithm for radar signal processing, and the present invention will not be specifically introduced.

[0068] Specifically, the obtaining of the channel signal by digital beamforming includes:

[0069]

[0070] In the formula, is the sum channel signal, for dimensional vector, representing the steering vector of each channel, ,in , represents the array element spacing, Indicates the current scanning angle. represents the phase normalization coefficient, is the phase difference between two adjacent array elements.

[0071] In an exemplary embodiment of the present invention, determining the range unit and Doppler unit where the swarm target is located includes:

[0072] Application Window Perform sliding window processing on the distance unit. If there is a target in each distance unit in the sliding window, the target set within the window coverage is determined to be a swarm target, indicating that there may be multiple small targets flying in coordination in the area, such as a drone cluster or other high-density flying objects similar to a swarm. The set of targets in which the swarm target is located is recorded. ,in , It is represented as the number of target swarms, generating a swarm target set, otherwise, no processing is performed.

[0073] Specifically, calculating the multi-channel autocorrelation matrix includes:

[0074]

[0075] In the formula, For the The detected target bee colony corresponds to dimensional multi-channel autocorrelation matrix, For hope, To find the conjugate transpose, In the signal matrix after channel calibration, the row number set is Column number is Channel signal, is a constant.

[0076] In an exemplary embodiment of the present invention, performing eigenvalue decomposition and noise subspace extraction includes:

[0077]

[0078] In the formula, Indicates The signal subspace of the target, represents the noise subspace, the diagonal matrix and They represent the eigenvalues ​​of the corresponding signal and the eigenvalues ​​of the noise respectively.

[0079] In an exemplary embodiment of the present invention, constructing a spatial spectrum function, performing spectrum peak search and target angle analysis includes:

[0080]

[0081] In the formula, is the spatial spectrum function, for Find the conjugate transpose of a dimensional vector, is the noise space;

[0082] In an exemplary embodiment of the present invention, traversing airspace targets and summarizing and outputting detection results include:

[0083] Perform a spectrum peak search on the spatial spectrum function to determine the spectrum peak position. Each spectrum peak position corresponds to several targets with different wave directions in the same range gate and Doppler gate, and generate a set consisting of the target. , until the swarm target set is completed All targets within are traversed and the process ends.

[0084] Gather all targets Perform summary output, complete the current echo signal frame processing, and realize the detection and resolution of all targets in the airspace.

[0085] In a second aspect, the present invention further provides a swarm target super-resolution detection system based on spatial spectrum signal analysis, comprising:

[0086] The signal processing module is configured to transmit a radio frequency signal to a target area through a radar transmitter, receive a radio frequency echo signal reflected by the target through a receiver, and convert the radio frequency echo signal into an intermediate frequency signal through a superheterodyne receiver; perform preprocessing operations on the antenna multi-channel receiving signals respectively to obtain processed signals, and perform consistency calibration on the multi-channel signals and the amplitude and phase errors between channels based on the intermediate frequency signals;

[0087] The detection module is configured to perform digital beam synthesis and target detection, obtain a target point trace set, perform sliding window detection on the target point trace set, and determine the range unit and Doppler unit where the swarm target is located; calculate the multi-channel autocorrelation matrix through the range unit and Doppler unit in the window where the swarm target is located, perform eigenvalue decomposition and noise subspace extraction on the multi-channel autocorrelation matrix, construct a spatial spectrum function, and perform spectrum peak search and target angle analysis;

[0088] The output module is configured to traverse the airspace targets and summarize the detection results and output them, completing the super-resolution detection of targets in the radar scanning airspace.

[0089] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0090] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0091] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A super-resolution detection method for swarm targets based on spatial spectrum signal analysis, characterized in that: include: The radar transmitter transmits a radio frequency signal to the target area, the receiver receives the radio frequency echo signal reflected by the target, and converts the radio frequency echo signal into an intermediate frequency signal through a superheterodyne receiver; Preprocessing operations are performed on several intermediate frequency signals received by the antenna multi-channel and converted by the superheterodyne receiver to obtain preprocessed signals, and consistency calibration of amplitude and phase errors between channels is performed based on the preprocessed signals; Perform digital beam synthesis and target detection to obtain a target point trace set, perform sliding window detection on the target point trace set, and determine the range unit and Doppler unit where the swarm target is located; The multi-channel autocorrelation matrix is ​​calculated through the range unit and Doppler unit in the window where the swarm target is located, and the eigenvalue decomposition and noise subspace extraction of the multi-channel autocorrelation matrix are performed to construct the spatial spectrum function, perform spectrum peak search and target angle analysis; The airspace targets are traversed and the detection results are summarized and output to complete the super-resolution detection of targets in the radar scanning airspace; The consistency calibration comprises: In the formula, is the amplitude calibration coefficient of each channel, is the phase calibration coefficient of each channel, are the signals of each channel after amplitude and phase consistency calibration, is the preprocessed signal, is the imaginary unit of the imaginary number; The digital beamforming and target detection comprises: The sum channel signals are obtained by digital beam synthesis, and the target detection algorithm is used to perform target detection on the sum channel signals to generate a set of target point traces in the scanning area; The obtaining of the channel signal by digital beamforming comprises: In the formula, is the sum channel signal, for dimensional vector, The number of receiving signal channels.

2. The method for super-resolution detection of swarm targets based on spatial spectrum signal analysis according to claim 1, characterized in that: The distance unit and Doppler unit for determining the location of the swarm target include: Application Window The distance units are processed by sliding windows. If there is a target in each distance unit in the sliding window, the target set within the coverage of the window is determined as a swarm target and a swarm target set is generated. Otherwise, no processing is performed.

3. The method for super-resolution detection of swarm targets based on spatial spectrum signal analysis according to claim 2, characterized in that: The eigenvalue decomposition and noise subspace extraction include: In the formula, Indicates The signal subspace of the target, represents the noise subspace, the diagonal matrix and They represent the eigenvalues ​​of the corresponding signal and the eigenvalues ​​of the noise respectively. For the The detected target bee colony corresponds to dimensional multi-channel autocorrelation matrix, is a constant.

4. The method for super-resolution detection of swarm targets based on spatial spectrum signal analysis according to claim 3, characterized in that: The constructing of the spatial spectrum function, performing spectrum peak search and target angle analysis comprises: In the formula, is the spatial spectrum function, for Find the conjugate transpose of a dimensional vector.

5. The method for super-resolution detection of swarm targets based on spatial spectrum signal analysis according to claim 4, characterized in that: The airspace target traversal and detection result summary output include: The spatial spectrum function is searched for spectral peaks to determine the spectral peak positions. Each spectral peak position corresponds to several targets with different wave directions in the same range gate and Doppler gate. A set consisting of the target is generated until all targets in the swarm target set are traversed and the process ends.

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