Gaussian background distance walk extension target CFAR detection method and system

Abstract

The Gauss background distance walking extended target CFAR detection method and system relate to the radar adaptive detection technical field, and aim at the problem that in the prior art, for large targets, the existing method will be dispersed in distance due to target energy, thereby causing the problem that the target cannot be effectively detected.The technical scheme of the present application proposes a generalized matching subspace detector (GMSD), which has CFAR characteristics for clutter under the Gaussian background, can effectively compensate the distance walking effect of large targets, and can effectively accumulate the energy of large targets dispersed at different distances, thereby effectively improving the detection capability for large targets.The technical scheme of the present application also proposes a method for fast calculation of GMSD, which is called simplified GMSD (SGMSD) detector.The detector can greatly reduce the operation amount of GMSD by approximately simplifying the matrix operation in the GMSD process, thereby realizing the fast calculation of GMSD.

Description

Technical Field

[0001] This invention relates to the field of radar adaptive detection technology, specifically to a method and system for detecting range-traveling extended targets with Gaussian background using CFAR. Background Technology

[0002] Radar detects targets by emitting electromagnetic waves and receiving their echo signals. Due to the relative motion between the target and the radar, radar echoes take on different forms. To improve radar detection capabilities, modern radar systems often use multiple pulse echoes for joint processing; the time required for one such joint processing is called the coherent processing interval. Compared to processing a single pulse individually, joint processing of multiple pulses can significantly improve the signal-to-noise ratio of the target echo, thereby increasing the probability of target detection and enhancing the radar's detection capabilities. To acquire more information about the target, modern radars increase the range resolution by increasing the bandwidth of the transmitted signal. The target is resolved into several discrete scattering points in the radar echo, and the distribution along the range dimension reflects the intensity information of the target's scattering points. The echo of a single pulse is called the target range profile.

[0003] Early radars had low range resolution, meaning targets could be considered to occupy only one resolution cell. Furthermore, within the coherent processing interval, targets were considered to exist within a single range resolution cell. The echoes from different pulses were assumed to differ only in phase, without any change in the echo envelope; that is, the target did not move range (RM) between pulses. Adaptive Matched Filter (AMF) detectors were proposed. AMF uses Adaptive Moving Target Detection (AMTD) as the numerator, while the normalization factor in the denominator gives AMF a constant false alarm rate (CFAR) characteristic against Gaussian clutter parameters.

[0004] However, as radar range resolution improves, the size of targets often exceeds the radar's range resolution, thus occupying multiple range cells in the echo. The target's energy is dispersed across multiple range cells, a phenomenon known as range spread (RD). Furthermore, due to the relative motion between the target and the radar, the target echo may exhibit range drift, posing challenges to the joint processing of echoes from multiple pulses.

[0005] To address the target range spread problem, the earliest N / M detector was proposed by Hughes. This is a noncoherent detector for single-pulse echoes, extracting several large scattering points in the range profile as estimates of the target scattering points, and then detecting these points. Later, Gerlach et al. proposed the SDD-GLRT detector, which, while also for single-pulse echoes, utilizes prior information about the target scattering point density. Conte et al. extended the AMF (Advanced Range Detection) to the case of range-spreading targets, thus expanding the applicability of the AMF detector; this method is called the RD-GAMF detector, a type of CFAR (Cross-Coherent Array Detector). However, the methods mentioned above do not consider target range movement, therefore their performance degrades significantly when the target's range movement is present.

[0006] To address the target range-moving problem, the Hough transform (HT) was the first proposed method. HT is a non-coherent accumulation method that only utilizes the amplitude information of the target echo, not its phase information. Later, the Keystone transform (KT) was proposed. KT is a coherent accumulation method that can utilize both the amplitude and phase information of the target echo, effectively improving radar detection performance compared to HT. However, KT requires complex interpolation calculations and suffers from Doppler ambiguity. To address the problems of KT, the Radon-Fourier transform (RFT), based on Radon and Fourier transforms, was proposed. This method can be viewed as a generalized filter bank, a generalization of moving target detection (MTD) in cases where the target is moving. However, both RFT and MTD assume a Gaussian white noise background, which limits the use of RFT in practical scenarios. To address this issue, Adaptive RFT (ARFT) was proposed. ARFT can be seen as a generalization of Adaptive Moving Target Detection (AMTD) to the range-moving case. Similar to AMTD, while this method can effectively suppress clutter, its output depends on the parameters of the clutter, meaning it is not a true CFAR (Continuous Moving Target Detection). Recently, we proposed a CFAR detector for range-moving point targets against a Gaussian background, which can be seen as an extension of AMF (Active Moving Target Detection) to the range-moving case. We call this the RM-GAMF detector. However, this method is only designed for point targets and cannot effectively accumulate the energy of the extended range target in the range dimension.

[0007] To address the simultaneous existence of range spread and range movement, Shui Penglang proposed a detection method based on the correlation between two adjacent echoes. However, this method only works for Gaussian white noise and lacks the CFAR characteristics for Gaussian distributions. Furthermore, some detection methods for non-Gaussian backgrounds have been proposed. For example, Xu Shuwen proposed a detection method based on long pulse train segmentation for the problem of range-moving target detection under a composite Gaussian distribution. Within a single sub-pulse train, the target can be considered to have only range spread and no range movement, thus simplifying the problem.

[0008] In summary, the existing Gaussian background distance CFAR detection method for moving point targets is only applicable to the detection of point targets. For large targets, the existing method will fail to effectively detect the target due to the dispersion of the target energy over distance. Summary of the Invention

[0009] The purpose of this invention is to address the problem that existing methods cannot effectively detect large targets due to the dispersion of target energy over distance, and to propose a Gaussian background distance-travel extended target CFAR detection method and system.

[0010] The technical solution adopted by the present invention to solve the above-mentioned technical problems is as follows:

[0011] The Gaussian background distance-based moving target CFAR detection method includes the following steps:

[0012] Step 1: Acquire radar echo data after A / D sampling, wherein the radar echo data includes data of the unit to be detected and data of the reference unit;

[0013] Step 2: Given the range of parameters for the uniform motion of the target of interest, take values ​​at equal intervals within the range of parameters for the uniform motion of the target, and each value is a parameter point;

[0014] Step 3: Select the i-th parameter point within the range of parameters for the uniform motion of the target. The value corresponding to this parameter point is (r i ,v i ), r i v represents the initial distance of the target relative to the radar. i Represents the target's velocity relative to the radar, and according to (r i ,v i Construct the target echo matrix, and then obtain the target echo tensor T based on the target echo matrix. R×M×K Where R represents the number of scattering points, M represents the number of pulses, K represents the detection window length, and T represents the target echo tensor. R×M×K satisfy:

[0015] T(j,:,:)=t j

[0016] Among them, t j Let T represent the echo matrix within the detection window corresponding to the j-th scattering point, where T is a tensor, and j = 1, ..., R;

[0017] Step 4: Obtain the target cell matrix Z and the reference cell matrix Z based on the target cell data and the reference cell data, respectively. L And satisfy Z L =[z1,…,z L ], where z1 represents the first reference element, z L Let Z represent the Lth reference cell, the cell matrix to be detected, and the reference cell matrix Z. L In the diagram, the horizontal axis represents different distance units, and the vertical axis represents different pulses;

[0018] Step 5: Using the reference unit matrix Z L Estimating the clutter covariance matrix And based on the estimated clutter covariance matrix Combined with the target echo tensor T R×M×K And the detection statistics are obtained from the matrix Z of the unit to be detected, and the clutter covariance matrix. Represented as:

[0019]

[0020] Where L represents the number of reference elements, z i Let (·) represent the i-th reference unit. H Represents the conjugate transpose of a matrix;

[0021] Step 6: Compare the detection statistic with the threshold. If the detection statistic exceeds the threshold, the parameter point is the target; otherwise, it is clutter.

[0022] Step 7: Remove the parameter points selected in Step 3 from the target uniform motion parameter range, and repeat Steps 3 to 6 to complete the judgment of each parameter point within the target uniform motion parameter range.

[0023] Furthermore, the detection statistics are expressed as follows:

[0024] Λ GMSD (Z)=b H A -1 b

[0025] Where A and b represent intermediate variables, tr(·) represents the trace of the matrix, and (·) H The conjugate transpose of a matrix is ​​represented by (·). -1 Represents the inverse of a matrix.

[0026]

[0027]

[0028] Furthermore, the detection statistics are expressed as follows:

[0029]

[0030] Where tr(·) represents the trace of the matrix, (·) H The conjugate transpose of a matrix is ​​represented by (·). -1 represents the inverse of a matrix, and |·| represents the modulus of a complex number.

[0031] Furthermore, the echo matrix t within the detection window corresponding to the j-th scattering point j Represented as:

[0032]

[0033] Where m represents the m-th pulse, n represents the n-th sampling point, α represents the target amplitude, and r j v represents the initial distance of the j-th scattering point. j ρ represents the velocity of the j-th scattering point. r f represents the radar range resolution. d T represents the Doppler frequency of the target. r This represents the pulse repetition period and satisfies the following relationship:

[0034] r j =r i +(j-1)ρ r

[0035] v j =v i

[0036] Where, r i v represents the initial distance corresponding to the i-th parameter point. i This represents the velocity corresponding to the i-th parameter point;

[0037] The radar range resolution ρ r Represented as:

[0038]

[0039] Where B represents bandwidth and c represents the speed of light;

[0040] The Doppler frequency f of the target d Represented as:

[0041]

[0042] Where λ represents the wavelength of the electromagnetic wave emitted by the radar, and v represents the velocity of the target relative to the radar.

[0043] Furthermore, the threshold value is obtained through a Monte Carlo experiment, and the elements in the clutter covariance matrix C of the Monte Carlo experiment satisfy:

[0044] C(i,j)=σ 2 ρ |i-j|

[0045] Where, σ 2 Let represent the average power of the clutter, ρ represent the first-order correlation coefficient, and i and j represent the elements in the covariance matrix C.

[0046] A Gaussian background range-traveling extended target CFAR detection system, the system comprising: a radar echo data acquisition module, a parameter point selection module, a detection statistics generation module, and a threshold comparison module;

[0047] The radar echo data acquisition module is used to acquire radar echo data after A / D sampling. The radar echo data includes data of the unit to be detected and data of the reference unit.

[0048] The parameter point selection module is used to specify a range of parameters for the uniform motion of the target of interest, and to select values ​​at equal intervals within this range. Each value is a parameter point. The i-th parameter point is selected within the range of the target's uniform motion parameters, and the value corresponding to this parameter point is (r... i ,v i ), r i v represents the initial distance of the target relative to the radar. i Represents the target's velocity relative to the radar, and according to (r i ,v i Construct the target echo matrix, and then obtain the target echo tensor T based on the target echo matrix. R×M×K Where R represents the number of scattering points, M represents the number of pulses, K represents the detection window length, and T represents the target echo tensor. R×M×K satisfy:

[0049] T(j,:,:)=t j

[0050] Among them, t j Let T represent the echo matrix within the detection window corresponding to the j-th scattering point, where T is a tensor, and j = 1, ..., R;

[0051] The detection statistics generation module is used to obtain the target cell matrix Z and the reference cell matrix Z based on the target cell data and the reference cell data, respectively. L And satisfy Z L =[z1,…,zL ], where z1 represents the first reference element, z L Let Z represent the Lth reference cell, the cell matrix to be detected, and the reference cell matrix Z. L In the diagram, the horizontal axis represents different distance units, and the vertical axis represents different pulses, using the reference unit matrix Z. L Estimating the clutter covariance matrix And based on the estimated clutter covariance matrix Combined with the target echo tensor T R×M×K And the detection statistics are obtained from the matrix Z of the unit to be detected, and the clutter covariance matrix. Represented as:

[0052]

[0053] Where L represents the number of reference elements, z i Let (·) represent the i-th reference unit. H Represents the conjugate transpose of a matrix;

[0054] The threshold comparison module is used to compare the detection statistics with the threshold. If the detection statistics exceed the threshold, the parameter point is the target; otherwise, it is clutter. The parameter points selected in step three are removed from the target uniform motion parameter range, and steps three to six are repeated to complete the judgment of each parameter point within the target uniform motion parameter range.

[0055] Furthermore, the detection statistics are expressed as follows:

[0056] Λ GMSD (Z)=b H A -1 b

[0057] Where A and b represent intermediate variables, tr(·) represents the trace of the matrix, and (·) H The conjugate transpose of a matrix is ​​represented by (·). -1 Represents the inverse of a matrix.

[0058]

[0059]

[0060] Furthermore, the detection statistics are expressed as follows:

[0061]

[0062] Where tr(·) represents the trace of the matrix, (·) H The conjugate transpose of a matrix is ​​represented by (·). -1 represents the inverse of a matrix, and |·| represents the modulus of a complex number.

[0063] Furthermore, the echo matrix t within the detection window corresponding to the j-th scattering point j Represented as:

[0064]

[0065] Where m represents the m-th pulse, n represents the n-th sampling point, α represents the target amplitude, and r j v represents the initial distance of the j-th scattering point. j ρ represents the velocity of the j-th scattering point. r f represents the radar range resolution. d T represents the Doppler frequency of the target. r This represents the pulse repetition period and satisfies the following relationship:

[0066] r j =r i +(j-1)ρ r

[0067] v j =v i

[0068] Where, r i v represents the initial distance corresponding to the i-th parameter point. i This represents the velocity corresponding to the i-th parameter point;

[0069] The radar range resolution ρ r Represented as:

[0070]

[0071] Where B represents bandwidth and c represents the speed of light;

[0072] The Doppler frequency f of the target d Represented as:

[0073]

[0074] Where λ represents the wavelength of the electromagnetic wave emitted by the radar, and v represents the velocity of the target relative to the radar.

[0075] Furthermore, the threshold value is obtained through a Monte Carlo experiment, and the elements in the clutter covariance matrix C of the Monte Carlo experiment satisfy:

[0076] C(i,j)=σ 2 ρ |i-j|

[0077] Where, σ 2 Let represent the average power of the clutter, ρ represent the first-order correlation coefficient, and i and j represent the elements in the covariance matrix C.

[0078] The beneficial effects of this invention are:

[0079] This application proposes a generalized matched subspace detector (GMSD). This detector has CFAR characteristics against clutter in a Gaussian background, which can effectively compensate for the range-walking effect of large targets. At the same time, it can effectively accumulate the energy dispersed by large targets at different distances, thereby effectively improving the detection capability of large targets.

[0080] Furthermore, this application also proposes a method for fast GMSD calculation, called the Simplified GMSD (SGMSD) detector. This detector can greatly reduce the amount of GMSD computation by approximately simplifying the matrix operations in the GMSD calculation process, thus achieving fast GMSD calculation. Attached Figure Description

[0081] Figure 1 Here is a flowchart of the GMSD detection process;

[0082] Figure 2 Here is a flowchart of the SGMSD detection process;

[0083] Figure 3 A schematic diagram for extracting data from the unit to be detected and the reference unit;

[0084] Figure 4 This is a schematic diagram of the false alarm probability curve for GMSD;

[0085] Figure 5 This is a schematic diagram of the false alarm probability curve for SGMSD.

[0086] Figure 6 A schematic diagram of point target echo without distance movement and a comparison of detection performance curves;

[0087] Figure 7 To illustrate the target's distance-free movement echo and compare detection performance curves;

[0088] Figure 8 A schematic diagram of the distance travel echo of a point target and a comparison of detection performance curves;

[0089] Figure 9 To illustrate the target distance travel echo diagram and the comparison of detection performance curves;

[0090] Figure 10 Functional diagrams for different methods. Detailed Implementation

[0091] It should be noted that, where there is no conflict, the various embodiments disclosed in this application can be combined with each other.

[0092] Specific implementation method one: Refer to Figure 1 This embodiment of the method for detecting moving targets with Gaussian background distance (CFAR) includes the following steps:

[0093] Step 1: Acquire radar echo data after A / D sampling, wherein the radar echo data includes data of the unit to be detected and data of the reference unit;

[0094] Step 2: Given the range of parameters for the uniform motion of the target of interest, take values ​​at equal intervals within the range of parameters for the uniform motion of the target, and each value is a parameter point;

[0095] Step 3: Select the i-th parameter point within the range of parameters for the uniform motion of the target. The value corresponding to this parameter point is (r i ,v i ), r i v represents the initial distance of the target relative to the radar. i Represents the target's velocity relative to the radar, and according to (r i ,v i Construct the target echo matrix, and then obtain the target echo tensor T based on the target echo matrix. R×M×K Where R represents the number of scattering points, M represents the number of pulses, K represents the detection window length, and T represents the target echo tensor. R×M×K satisfy:

[0096] T(j,:,:)=t j

[0097] Among them, t j Let (j,:,:) represent the echo matrix within the detection window corresponding to the j-th scattering point. Let (j,:,:) represent a slice of the 3D data. The tensor is a data cube, and (j,:,:) represents the j-th slice of the cube. Each layer of the cube is a matrix representing the matrix of a scattering point. The matrices of all scattering points are superimposed to form a tensor.

[0098] Step 4: Obtain the matrix Z of the target cell and the matrix Z of the reference cell based on the target cell data and the reference cell data, respectively. L And satisfy Z L =[z1,…,z L ], where z1 represents the first reference element, z L Z represents the Lth reference element, and the reference element matrix is ​​Z. L The horizontal axis represents different distance units, and the vertical axis represents different pulses;

[0099] Step 5: Using the reference unit matrix Z L Estimating the clutter covariance matrix And based on the estimated clutter covariance matrix Combined with the target echo tensor T R×M×K And the detection statistics are obtained from the matrix Z of the units to be detected;

[0100] Step 6: Compare the detection statistic with the threshold. If the detection statistic exceeds the threshold, the parameter point is the target; otherwise, it is clutter.

[0101] Step 7: Remove the parameter points selected in Step 3 from the target uniform motion parameter range, and repeat Steps 3 to 6 to complete the judgment of each parameter point within the target uniform motion parameter range.

[0102] Figure 3 A schematic diagram of the steps in this application is provided, where the horizontal axis represents different distance cells and the vertical axis represents different pulses. That is, the same row represents the echo of a pulse from different distance cells, and the same column represents the echo of a pulse from different pulses from the same distance cell. Solid squares represent parameter values ​​(r). i ,v i The black square represents the resolution cell occupied by the target in the echo; the black square represents the resolution cell occupied by clutter echo; the gray square is the detection window.

[0103] The effectiveness of the algorithm was verified using Monte Carlo simulation experiments. The radar pulse width T was set. P =10μs, bandwidth is B=30MHz, sampling frequency f s =B=30MHz, carrier frequency is f c =150MHz, pulse repetition period is T r =0.5ms. The expression for the target echo matrix at this time is as follows:

[0104]

[0105] Where m represents the m-th pulse, n represents the n-th sampling point, and α is the target amplitude. r0 is the initial distance of the target relative to the radar. Let c be the radar range resolution and c be the speed of light. Let be the Doppler frequency of the target, and v be the target's velocity relative to the radar. The clutter simulation follows a complex Gaussian distribution with a covariance matrix C, and its elements satisfy:

[0106] C(i,j)=σ 2 ρ |i-j|

[0107] Where σ 2 Let ρ be the average power of the clutter, and let ρ be the first-order correlation coefficient, which is related to the bandwidth of the clutter.

[0108] Experiment 1: Simulation of false alarm characteristics of GMSD and SGMSD. The initial target distance was set to r0 = 2000m, the velocity to v = 680m / s, the echo pulse number to M = 64, and the number of reference elements to L = 150. The false alarm probability as a function of the threshold was simulated under different clutter parameters. The false alarm characteristic curve of GMSD is shown below. Figure 4 As shown, the false alarm characteristic curve of SGMSD is as follows: Figure 5 As shown in the results, both methods of the present invention have CFAR characteristics for clutter parameters.

[0109] Experiment 2: Performance Comparison of Point Target Moving Target Detection Without Distance. Clutter parameters were set as σ = 1, ρ = 0.9, initial target distance was set as r0 = 2000m, velocity as v = 0m / s, and echo pulse number as M = 64. It was also assumed that the covariance matrix of the clutter was known. A schematic diagram of the target echo is shown below. Figure 6 In (a), it can be seen that the target occupies a single distance cell and does not move any distance. Figure 6 Figure (b) shows the detection curves for different methods. It can be seen that when the point target does not move, the detection performance of the five methods is almost the same.

[0110] Experiment 3: Performance Comparison of Extended Target Detection Without Range Movement. Clutter parameters were set to σ = 1, ρ = 0.9, and the target occupied R = 4 consecutive range cells in the range dimension. The initial distance of the first scattering point was r0 = 2000m, and its velocity was v = 0m / s. The echo pulse number was set to M = 64, and the covariance matrix of the clutter was assumed to be known. A schematic diagram of the target echo is shown below. Figure 7 In (a), it can be seen that the target occupies 4 distance units and does not move any distance. Figure 7 Figure (b) shows the detection curves of different methods. It can be seen that when the extended target does not move at any distance, the GMSD and SGMSD methods proposed in this application, as well as RD-GAMF, achieve the best detection performance.

[0111] Experiment 4: Performance Comparison of Point Target Detection for Moving Targets. Clutter parameters σ = 1, ρ = 0.9, initial target distance r0 = 2000m, velocity v = 680m / s, echo pulse number M = 64, and the covariance matrix of the clutter is assumed to be known. Target echo diagram is shown below. Figure 8 In (a), it can be seen that the target occupies a single distance cell and has distance movement. Figure 8 Figure (b) shows the detection curves of different methods. It can be seen that when the target distance moves, the GMSD and SGMSD methods proposed in this application, as well as RM-GAMF, achieve the best detection performance.

[0112] Experiment 5: Performance Comparison of Moving Target Detection at Extended Target Range. Clutter parameters σ = 1, ρ = 0.9, and the target occupying R = 4 consecutive range cells in the range dimension. The initial distance of the first scattering point is r0 = 2000m, and its velocity is v = 680m / s. The echo pulse number is set to M = 64, and it is assumed that the covariance matrix of the clutter is known. A schematic diagram of the target echo is shown below. Figure 9 (a) shows that the target occupies 4 distance units and has distance movement. Figure 9 (b) Detection curves for different methods are shown. It can be seen that, under the condition of extended target distance movement, the GMSD and SGMSD methods proposed in this application achieve the best detection performance.

[0113] Finally, to summarize the various methods, such as Figure 10 As shown, compared with existing methods, GMSD and SGMSD have the following four characteristics: (1) They can simultaneously utilize the amplitude and phase information of the target echo, thus enabling effective energy accumulation for range-moving targets; (2) They can suppress Gaussian clutter, thus enabling effective application in actual clutter scenarios; (3) The output has invariance to clutter parameters, i.e., it has CFAR characteristics for clutter parameters. This characteristic can ensure that the false alarm rate of the radar detector remains unchanged, which has important practical engineering significance; (4) Under the condition of known target scattering point range dimension distribution information, it can effectively accumulate target energy along the range dimension. In addition, SGMSD can greatly reduce the amount of GMSD computation by approximately simplifying the matrix operations in the GMSD calculation process, thus realizing fast GMSD calculation.

[0114] It should be noted that the specific embodiments are merely explanations and illustrations of the technical solution of the present invention and should not be used to limit the scope of protection. Any modifications made in accordance with the claims and specification of the present invention that are only partial should still fall within the protection scope of the present invention.

Claims

1. A Gaussian background distance-based moving extended target CFAR detection method, characterized in that... Includes the following steps: Step 1: Acquire radar echo data after A / D sampling, wherein the radar echo data includes data of the unit to be detected and data of the reference unit; Step 2: Given the range of parameters for the uniform motion of the target of interest, take values ​​at equal intervals within the range of parameters for the uniform motion of the target, and each value is a parameter point; Step 3: Select the i-th parameter point within the range of parameters for the target's uniform motion. The value corresponding to this parameter point is... , This indicates the initial distance of the target relative to the radar. Indicates the target's velocity relative to the radar, and according to Construct the target echo matrix, and then obtain the target echo tensor based on the target echo matrix. ,in, Indicates the number of scattering points. Indicates the number of pulses. Indicates the detection window length and the target echo tensor. satisfy: in, Indicates the first The echo matrix within the detection window corresponding to each scattering point That is, tensor, which represents a tensor. =1,..., ; Step 4: Obtain the matrix of the cells to be detected based on the data of the cells to be detected and the data of the reference cells. and reference unit matrix and satisfy ,in Indicates the first reference unit. Indicates the first Reference cells, matrix of cells to be detected and reference unit matrix In the diagram, the horizontal axis represents different distance units, and the vertical axis represents different pulses; Step 5: Utilize the reference unit matrix Estimating the clutter covariance matrix And based on the estimated clutter covariance matrix Combined with target echo tensor and the matrix of units to be detected The detection statistics are obtained, and the clutter covariance matrix is... Represented as: in, Indicates the number of reference units. Indicates the first One reference unit, Represents the conjugate transpose of a matrix; Step 6: Compare the detection statistic with the threshold. If the detection statistic exceeds the threshold, the parameter point is the target; otherwise, it is clutter. Step 7: Remove the parameter points selected in Step 3 from the range of the target uniform motion parameters, and repeat Steps 3 to 6 to complete the judgment of each parameter point within the range of the target uniform motion parameters. The detection statistics are expressed as follows: in, and Indicates intermediate variables. Represents the trace of a matrix. This represents the conjugate transpose of a matrix. Represents the inverse of a matrix. ， ； Or the detection statistic is expressed as: in, Represents the trace of a matrix. This represents the conjugate transpose of a matrix. Represents the inverse of a matrix. Represents the modulus of a complex number.

2. The Gaussian background distance-based moving extended target CFAR detection method according to claim 1, characterized in that... The first The echo matrix within the detection window corresponding to each scattering point Represented as: in, Indicates the first One pulse, Indicates the first One sampling point, Indicates the target range. Indicates the first The initial distance between the scattering points Indicates the first The velocity of each scattering point Indicates radar range resolution. Indicates the Doppler frequency of the target. This represents the pulse repetition period and satisfies the following relationship: in, Indicates the first The initial distances corresponding to the parameter points, Indicates the first The velocity corresponding to each parameter point; The radar range resolution Represented as: in, Indicates bandwidth. Represents the speed of light; The Doppler frequency of the target Represented as: in, The wavelength of the electromagnetic waves emitted by radar. This indicates the target's velocity relative to the radar.

3. The Gaussian background distance-based moving extended target CFAR detection method according to claim 1, characterized in that... The threshold value is obtained through a Monte Carlo experiment, and the clutter covariance matrix in the Monte Carlo experiment is... The elements in satisfy: in, This represents the average power of the clutter. This represents the first-order correlation coefficient. and Represents the covariance matrix The elements in.

4. A Gaussian background distance-based moving target CFAR detection system, characterized in that... The system includes: a radar echo data acquisition module, a parameter point selection module, a detection statistics generation module, and a threshold comparison module. The radar echo data acquisition module is used to acquire radar echo data after A / D sampling. The radar echo data includes data of the unit to be detected and data of the reference unit. The parameter point selection module is used to specify a range of parameters for the uniform motion of the target of interest, and to select values ​​at equal intervals within this range. Each value is a parameter point. The i-th parameter point is selected within the range of the target's uniform motion parameters, and the value corresponding to this parameter point is... , This indicates the initial distance of the target relative to the radar. Indicates the target's velocity relative to the radar, and according to Construct the target echo matrix Then, based on the target echo matrix Obtain the target echo tensor ,in, Indicates the number of scattering points. Indicates the number of pulses. Indicates the detection window length and the target echo tensor. satisfy: in, Indicates the first The echo matrix within the detection window corresponding to each scattering point That is, tensor, which represents a tensor. =1,..., ; The detection statistics generation module is used to obtain the matrix of the unit to be detected based on the data of the unit to be detected and the reference unit data, respectively. and reference unit matrix and satisfy ,in Indicates the first reference unit. Indicates the first Reference cells, matrix of cells to be detected and reference unit matrix In the diagram, the horizontal axis represents different distance cells, and the vertical axis represents different pulses, using a reference cell matrix. Estimating the clutter covariance matrix And based on the estimated clutter covariance matrix Combined with target echo tensor and the matrix of units to be detected The detection statistics are obtained, and the clutter covariance matrix is... Represented as: in, Indicates the number of reference units. Indicates the first One reference unit, Represents the conjugate transpose of a matrix; The threshold comparison module is used to compare the detection statistics with the threshold. If the detection statistics exceed the threshold, the parameter point is the target; otherwise, it is clutter. The parameter points selected in step three are removed from the target uniform motion parameter range, and steps three to six are repeated to complete the judgment of each parameter point within the target uniform motion parameter range. The detection statistics are expressed as follows: in, and Indicates intermediate variables. Represents the trace of a matrix. This represents the conjugate transpose of a matrix. Represents the inverse of a matrix. ， ； Or the detection statistic is expressed as: in, Represents the trace of a matrix. This represents the conjugate transpose of a matrix. Represents the inverse of a matrix. Represents the modulus of a complex number.

5. The Gaussian background distance-based moving extended target CFAR detection system according to claim 4, characterized in that... The first The echo matrix within the detection window corresponding to each scattering point Represented as: in, Indicates the first One pulse, Indicates the first One sampling point, Indicates the target range. Indicates the first The initial distance between the scattering points Indicates the first The velocity of each scattering point Indicates radar range resolution. Indicates the Doppler frequency of the target. This represents the pulse repetition period and satisfies the following relationship: in, Indicates the first The initial distances corresponding to the parameter points, Indicates the first The velocity corresponding to each parameter point; The radar range resolution Represented as: in, Indicates bandwidth. Represents the speed of light; The Doppler frequency of the target Represented as: in, The wavelength of the electromagnetic waves emitted by radar. This indicates the target's velocity relative to the radar.

6. The Gaussian background distance-based moving extended target CFAR detection system according to claim 4, characterized in that... The threshold value is obtained through a Monte Carlo experiment, and the clutter covariance matrix in the Monte Carlo experiment is... The elements in satisfy: in, This represents the average power of the clutter. This represents the first-order correlation coefficient. and Represents the covariance matrix The elements in.

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