A radar detection and multi-dimensional parameter estimation method and system for high-maneuvering group targets

Abstract

The application discloses a radar detection and multi-dimensional parameter estimation method and system for high-maneuvering group targets, and the method comprises the following steps: constructing a radar echo signal model of the high-maneuvering group targets; obtaining a pulse compression echo signal of the high-maneuvering group targets by using a pulse compression technology; performing signal energy coherent accumulation through second-order phase compensation, slow-time dimension non-uniform fast Fourier transform and distance dimension inverse fast Fourier transform, so as to realize group target detection and distance-velocity-acceleration joint coarse estimation; constructing an over-complete dictionary according to the multi-dimensional parameter coarse estimation result, and performing sparse representation on the pulse compression signal of the high-maneuvering group targets; and adopting a sparse Bayesian learning method to perform sparse reconstruction on distance-velocity-acceleration estimation space of each target signal in the group, so as to obtain fine estimation results of multi-dimensional parameters of each target. The application can quickly detect the high-maneuvering group targets without multi-dimensional search, realizes coarse-to-fine estimation of multi-dimensional parameters of each target, and takes into account estimation accuracy and operation efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to radar signal processing technology, in particular to a radar detection and multi-dimensional parameter estimation method and system for high-maneuvering group targets. BACKGROUND

[0002] With the rapid development of aerospace technology, high-maneuvering aircrafts such as unmanned aerial vehicles are increasingly present in the field of radar detection. Such targets have small radar cross section (RCS), are flexible and maneuverable, and can efficiently and accurately complete various specific tasks through high-density and large-scale deployment. Long-term observation is beneficial to the detection of weak targets by radar, but the high-maneuvering motion of the targets can cause range migration and Doppler migration of the echoes, causing the signal energy to spread along the range dimension and the velocity dimension, respectively, and reducing the performance of traditional moving target detection (MTD) methods. In addition, group targets that form dense formations often have very similar motion models, and the energy spectrum peaks of different targets can easily fall within the same range-velocity-acceleration resolution cell, making it difficult to detect and estimate the parameters of each target separately.

[0003] For the detection and parameter estimation of high-maneuvering targets, although some scholars have proposed algorithms such as generalized Radon-Fourier transform (GRFT) and polynomial Radon-polynomial Fourier transform, which can correct range-Doppler migration and coherently accumulate echo energy through joint search of target range, velocity, and acceleration, the computational complexity of multi-dimensional parameter traversal search is huge, and these methods are subject to resolution and do not have the ability to finely detect and estimate the parameters of group targets. SUMMARY

[0004] The present application relates to radar signal processing technology, in particular to a radar detection and multi-dimensional parameter estimation method and system for high-maneuvering group targets.

[0005] The technical solution for achieving the present application is as follows: In a first aspect, the present application provides a radar detection and multi-dimensional parameter estimation method for high-maneuvering group targets, comprising the following steps:

[0006] Constructing a radar echo signal model for high-maneuvering group targets;

[0007] Obtaining a pulse compression echo signal for high-maneuvering group targets using pulse compression technology;

[0008] Performing signal energy coherent accumulation through second-order phase compensation, slow-time NUFFT, and range IFFT to achieve group target detection and range-velocity-acceleration joint coarse estimation;

[0009] According to the multi-dimensional parameter rough estimation result, an over-complete dictionary is constructed, and the pulse compression signal of the high-maneuvering group target is sparsely represented.

[0010] A sparse Bayesian learning method is used to sparsely reconstruct the distance-speed-acceleration estimation space of each target signal in the cluster, so as to obtain the fine estimation result of the multi-dimensional parameter of each target.

[0011] In the second aspect, the present application provides a radar detection and multi-dimensional parameter estimation system for a high-maneuvering group target, which is used to realize the method in the first aspect, and the system comprises:

[0012] A first module is used to construct a radar echo signal model of the high-maneuvering group target.

[0013] A second module is used to obtain the pulse compression echo signal of the high-maneuvering group target by using the pulse compression technology.

[0014] A third module is used to realize the group target detection and the joint rough estimation of the distance-speed-acceleration by performing signal energy coherent accumulation through the second-order phase compensation, the slow-time NUFFT and the distance IFFT.

[0015] A fourth module is used to construct an over-complete dictionary according to the multi-dimensional parameter rough estimation result, and to sparsely represent the pulse compression signal of the high-maneuvering group target.

[0016] A fifth module is used to sparsely reconstruct the distance-speed-acceleration estimation space of each target signal in the cluster by using the sparse Bayesian learning method, so as to obtain the fine estimation result of the multi-dimensional parameter of each target.

[0017] In the third aspect, the present application provides an electronic device, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, and the processor realizes the steps of the method in the first aspect when executing the program.

[0018] In the fourth aspect, the present application provides a computer readable storage medium, which stores a computer program, and the program realizes the steps of the method in the first aspect when executed by a processor.

[0019] In the fifth aspect, the present application provides a computer program product, which comprises a computer program, and the computer program realizes the steps of the method in the first aspect when executed by a processor.

[0020] Compared with existing technologies, the significant advantages of this invention are as follows: For highly maneuverable group targets under the combined influence of range and Doppler migration, this invention combines second-order phase compensation, slow-time dimension NUFFT, and range dimension IFFT. Echo migration correction and coherent accumulation can be achieved simply by performing an acceleration search, thereby obtaining target detection and coarse parameter estimation results, avoiding multi-dimensional parameter search. Based on this, an overcomplete dictionary is constructed around the coarse estimation results and used to sparsely represent the group target signals. Then, multi-dimensional sparse reconstruction is achieved through sparse Bayesian learning to obtain refined estimation results for each target parameter, avoiding the problem of insufficient group target resolution performance in existing methods. Attached Figure Description

[0021] Figure 1 This is a flowchart of the radar detection and multi-dimensional parameter estimation method for highly maneuverable group targets according to the present invention.

[0022] Figure 2 This is a schematic diagram of the trajectory of a highly maneuverable group of targets after pulse compression.

[0023] Figure 3 This is a schematic diagram of the processing results using the MTD method.

[0024] Figure 4 This is a schematic diagram of the acceleration search results obtained by the method of the present invention.

[0025] Figure 5 This is a schematic diagram of the distance-velocity coherent accumulation result of the method of the present invention.

[0026] Figure 6 This is a schematic diagram of the three-dimensional sparse reconstruction result of the pulse compression signal of a highly maneuverable group target.

[0027] Figure 7 This is a schematic diagram showing the change of detection probability as a function of input signal-to-noise ratio for the method of this invention and the MTD method. Detailed Implementation

[0028] This invention proposes a radar detection and multi-dimensional parameter estimation method for highly maneuverable group targets. The following section combines... Figure 1 The steps of the method of the present invention will be described in detail.

[0029] like Figure 1 As shown, a radar detection and multi-dimensional parameter estimation method for highly maneuverable group targets includes the following steps:

[0030] Step 1, let Indicates the number of targets, and then the radar echo signal model of highly maneuverable group targets. Build as

[0031] ;

[0032] in, and These represent the fast-time variable and the slow-time variable, respectively. , , , , and These represent the pulse number, pulse repetition period, pulse width, speed of light, frequency modulation slope, and wavelength, respectively. For the goal The echo amplitude, Indicate target Relative radar instantaneous slant range, , and The target The initial slant range, radial velocity, and acceleration relative to the radar.

[0033] Step 2: High-mobility group target pulse compression echo signal obtained after pulse compression Represented as

[0034] ;

[0035] in, For the goal The amplitude of the pulse pressure signal, , and These are the distance-frequency variable, signal bandwidth, and carrier frequency, respectively.

[0036] according to It can be seen that, due to the distance frequency variable and slow time variables There is coupling between the target and the velocity components. The target's velocity and acceleration components will cause range migration in the echo, resulting in signal energy spreading along the range dimension. Additionally, the target's acceleration components will cause Doppler migration in the echo, causing signal energy to spread along the velocity dimension. Range and Doppler migration will make coherent energy accumulation of echoes from highly maneuverable targets difficult, severely reducing the target detection performance of traditional MTD methods.

[0037] Step 3: Obtain the coherent accumulation result of signal energy through second-order phase compensation, slow-time dimension NUFFT, and distance dimension IFFT. Represented as

[0038] ;

[0039] in, For azimuth frequency variables, To search for values ​​based on acceleration The constructed second-order phase compensation function, .

[0040] When the acceleration search value matches the actual target acceleration value The second-order phase term corresponding to the acceleration will be accurately compensated at this time. The maximum peak value will be output, and its expression is:

[0041] ;

[0042] in, For the goal The output signal amplitude.

[0043] For the coherent accumulation results, the constant false alarm rate (CFAR) and the position of the main peak can be used to achieve joint coarse estimation of target detection and distance-velocity-acceleration.

[0044] Step 4: Construct an overcomplete dictionary based on the coarse estimates of the target's multidimensional parameters. ,in and These represent the number of pulses and the number of sampling points in the distance dimension, respectively. , and The dictionary grids for range, velocity, and acceleration dimensions are respectively defined. Then, the pulse compression signal of a highly maneuverable group of targets is vectorized and sparsely represented as follows:

[0045] ;

[0046] in and Represent the observed signal vector and the sparse vector to be reconstructed, respectively. The noise vector has zero mean and zero variance. The Gaussian distribution.

[0047] Step 5: Using a sparse Bayesian learning method, super-resolution sparse reconstruction of the target signals within the cluster in the range-velocity-acceleration estimation space is performed. The parameter iteration process can be expressed as follows:

[0048] ;

[0049] in for The mean of the posterior distribution. The standard deviation of noise. for The covariance matrix of the posterior distribution. , for The variance vector, and The first and second parts are respectively the first and second parts before and after the update. One hyperparameter, for The diagonal elements, for The Each element.

[0050] After the iteration is complete, Inverse vectorization is performed to obtain sparse reconstruction results of high-maneuverability group target signals in the three-dimensional estimation space of range-velocity-acceleration. Based on the coordinates of each reconstruction point, accurate estimates of the range, velocity, and acceleration of each target within the group can be obtained.

[0051] The present invention will be further described below with reference to specific embodiments.

[0052] The radar transmission signal carrier frequency was set to 0.3 GHz, signal bandwidth to 6 MHz, pulse width to 25 μs, pulse repetition frequency to 2000 Hz, coherent accumulation time to 1 s, and echo signal-to-noise ratio to -20 dB. The range, velocity, and acceleration parameters of each target within the cluster are shown in Table 1.

[0053] Table 1 Target parameters within the cluster

[0054]

[0055] Figure 2 The trajectory of a highly maneuverable group of targets after pulse compression is presented, showing that the highly maneuverable movement of the targets leads to a significant distance migration phenomenon. Figure 3 The results of the MTD method show that, due to the combined effects of range and Doppler migration, the echo energy of the group targets diffuses in the range-velocity dimension and cannot be effectively accumulated. Figure 4 and Figure 5 The acceleration search results and corresponding range-velocity coherent accumulation results of the method of the present invention are given respectively. It can be seen that the influence of range and Doppler migration on echo energy accumulation has been eliminated. However, due to the limitation of parameter estimation resolution, only the parameters of each dimension of target 2 in the cluster can be estimated.

[0056] Based on the construction of an overcomplete dictionary around the coarse estimation results, Figure 6 The results of three-dimensional sparse reconstruction of the pulse compression signal of a highly maneuverable group target are presented, where the dictionary grid spacing for range, velocity, and acceleration is 5 m, 0.1 m / s, and 0.1 m / s, respectively. 2 It can be seen that the three targets within the cluster were effectively distinguished, and the distance, velocity, and acceleration of each target were accurately estimated.

[0057] Set the false alarm probability to 10. -6 , Figure 7The curves showing the detection probability versus input signal-to-noise ratio (SNR) for the proposed method and the MTD method are presented, with 500 Monte Carlo experiments performed for each input SNR. As can be seen from the figures, the proposed method exhibits superior target detection performance compared to the MTD method due to its joint compensation capability for range and Doppler migration. Specifically, when the detection probability is 0.9, the proposed method shows an approximately 19 dB SNR gain compared to the MTD method.

Claims

1. A radar detection and multi-dimensional parameter estimation method for high maneuvering group targets, characterized in that, The method comprises the following steps: constructing a radar echo signal model of the high-maneuvering group target; obtaining a pulse compression echo signal of the high-maneuvering group target by using a pulse compression technology; The signal energy coherent accumulation is performed through second-order phase compensation, slow-time NUFFT and distance-time IFFT, so as to realize group target detection and distance-speed-acceleration joint coarse estimation; and the signal energy coherent accumulation result is represented as ; wherein, and denote fast time variable and slow time variable, respectively, , denote pulse number and pulse repetition period, respectively, is an azimuth frequency variable, is a high maneuvering group target pulse compression echo signal after pulse compression, is a second-order phase compensation function, is an acceleration search value, denotes the speed of light, , and are a range frequency variable and a carrier frequency, respectively; When the acceleration search value matches the target acceleration value, The accumulated peak value is maximum, and the group target detection and distance-speed-acceleration joint rough estimation are realized based on the constant false alarm rate processing and the main peak position. constructing an over-complete dictionary according to a multi-dimensional parameter coarse estimation result, and performing sparse representation on the pulse compression signal of the high-maneuvering group target; performing sparse reconstruction on the distance-velocity-acceleration estimation space of each target signal in the group by using a sparse Bayesian learning method, and obtaining a multi-dimensional parameter fine estimation result of each target.

2. The radar detection and multi-dimensional parameter estimation method for high maneuvering group targets according to claim 1, characterized in that, Highly maneuverable group target echo model is constructed is represented as ; wherein, , , and represent pulse width, signal frequency modulation slope, signal wavelength and target number, respectively; is the echo amplitude of the target , represents the instantaneous slant range of the target to the radar, , and are the initial slant range, radial velocity and acceleration of the target to the radar, respectively.

3. The radar detection and multi-dimensional parameter estimation method for high maneuvering group targets according to claim 2, characterized in that, High maneuvering group target pulse compression echo signal obtained after pulse compression is represented as ; wherein, is the target pulse pressure signal amplitude, is the signal bandwidth.

4. The method of claim 1, wherein, The sparse representation form of the vectorized high-maneuvering group target pulse compression signal is ; wherein and respectively denote the observation signal vector and the sparse vector to be reconstructed, is a noise vector, obeying a zero-mean, variance Gaussian distribution; is an overcomplete dictionary built around the coarse estimate of the target parameter.

5. The radar detection and multi-dimensional parameter estimation method for high maneuvering group targets according to claim 4, characterized in that, The sparse Bayesian learning iteration process of the high-maneuvering group target signal is ; in for The mean of the posterior distribution. The standard deviation of noise. for The covariance matrix of the posterior distribution. , for The variance vector, and The first and second parts are respectively the first and second parts before and after the update. One hyperparameter, for The diagonal elements, for The One element; and These are the number of pulses and the number of sampling points in the distance dimension, respectively. When the iteration is completed, inverse vectorization is performed on to obtain a high-maneuvering group target signal sparse reconstruction result in a distance-velocity-acceleration three-dimensional estimation space, thereby realizing fine estimation of multi-dimensional parameters of each target.

6. A radar detection and multi-dimensional parameter estimation system for high maneuvering group targets, characterized by, The system is used for implementing the method of any one of claims 1-5, and comprises: a first module configured to construct a radar echo signal model of the high-maneuvering group target; a second module configured to obtain a pulse compression echo signal of the high-maneuvering group target by using a pulse compression technology; The third module carries out signal energy coherent accumulation through second-order phase compensation, slow-time dimension NUFFT and distance dimension IFFT, realizes group target detection and distance-speed-acceleration joint coarse estimation, and the signal energy coherent accumulation result is represented as is represented as ; wherein, and represent fast time variable and slow time variable respectively, , represent pulse number and pulse repetition period respectively, is an azimuth frequency variable, is a high maneuvering group target pulse compression echo signal after pulse compression, is a second order phase compensation function, is an acceleration search value, represents the speed of light, , and are a range frequency variable and a carrier frequency respectively; When the acceleration search value matches the target acceleration value, The accumulated peak value is maximum, and the group target detection and distance-speed-acceleration joint rough estimation are realized based on the constant false alarm rate processing and the main peak position. a fourth module configured to construct an over-complete dictionary according to a multi-dimensional parameter coarse estimation result, and perform sparse representation on the pulse compression signal of the high-maneuvering group target; a fifth module configured to perform sparse reconstruction on the distance-velocity-acceleration estimation space of each target signal in the group by using a sparse Bayesian learning method, and obtain a multi-dimensional parameter fine estimation result of each target.

7. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the program to implement the steps of the method of any one of claims 1-5.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the steps of the method of any one of claims 1-5.

9. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method of any one of claims 1-5.

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