Target adaptive detection method for quasi-whitening and interference smart suppression

Abstract

The application discloses a target adaptive detection method of quasi-whitening and intelligent interference suppression, and belongs to the field of wideband radar signal processing. In the case that external interference exists and the amount of uniform auxiliary data is small, in view of the problem that multi-channel wideband radar range expansion target detection is difficult to balance detection performance and mismatch robustness, the clutter skew-symmetric covariance matrix structure is reasonably utilized, the estimation accuracy of unknown clutter covariance matrix is improved, and the demand for the amount of uniform auxiliary data is effectively reduced, thereby providing strong support for realizing target adaptive detection under the condition of small sample of existing interference; a two-step detector design procedure is adopted, a target adaptive detection method of quasi-whitening and intelligent interference suppression with a closed form is constructed based on a GLRT test criterion, while the constant false alarm rate characteristic is ensured, the algorithm calculation complexity, intelligent anti-interference, mismatch robustness and detection performance and other aspects are considered, and the method has the value of popularization and application.

Description

Technical Field

[0001] This invention belongs to the field of radar signal processing technology, specifically relating to an adaptive target detection method with quasi-whitening and intelligent interference suppression. Background Technology

[0002] With the increase in radar bandwidth, its range resolution has been further improved. Wideband radar is widely used in modern military and civilian fields such as anti-jamming, counter-reconnaissance, precision detection and imaging, high-precision tracking, and target recognition. Adaptive detection of range-extended targets in wideband radar has become one of the hot issues in the radar field. Unlike narrowband radar, where target echo signals usually occupy only one range resolution cell, the energy of a target scattering point in wideband radar may spread to adjacent range cells, presenting a "one-dimensional range image" and forming a range-extended target. If point target detection methods are still used to detect targets on echo signals for individual range cells and to estimate background clutter statistical characteristics by sampling from neighboring range cells, on the one hand, the energy of strong scattering points of range-extended targets is easily leaked into adjacent range cells, leading to signal contamination and further obscuring the target signal of a single range cell, resulting in poor performance of point target detection methods. On the other hand, in practical applications, radar detection faces complex electromagnetic environments, which may include natural or man-made interference sources such as electronic countermeasures signals or various civilian electromagnetic signals. In addition, the complex and variable environment of the target enhances the non-uniformity of background clutter, and the number of pure clutter auxiliary data that meet the requirements of independent and identical distribution is relatively limited. Compared with narrowband radar, this problem is particularly prominent in broadband radar target detection scenarios, making it difficult for existing range-extended target detection methods to achieve ideal detection results.

[0003] Furthermore, multi-channel adaptive target detection under composite Gaussian clutter with unknown covariance matrices has always been a hot research topic. It typically assumes that clutter components in observation data from multiple range cells (also known as master data) are modeled by a composite Gaussian process with unknown covariance matrices and random textures (following an inverse gamma distribution). It also assumes the existence of a set of reference range cell data (also known as auxiliary data) without target signals, which can be used to estimate the unknown clutter covariance matrix, and that the clutter in the auxiliary data is also modeled by a composite Gaussian process with unknown covariance matrices and random textures (following an inverse gamma distribution). In practical applications, target signal steering vectors may mismatch due to wavefront distortion, array calibration errors, etc. For applications such as radar search modes, the detector needs strong robustness to mismatched signals; however, for commonly used rank-1 signal models, the target steering vector is fixed and completely known, making it difficult to address the aforementioned mismatch problem. If a detector with a known clutter covariance matrix is ​​constructed based on the master data of multiple range cells to be detected for a range-extended target, and then the unknown clutter covariance matrix is ​​estimated using auxiliary data, a range-extended target GLRT detector (S-GLRT) under a compound Gaussian environment can be obtained. This detector can achieve a certain level of robustness when the amount of auxiliary data is sufficient. However, in practical applications, the amount of auxiliary data is often limited, making it difficult for this detector to function effectively. Considering the difficulty in obtaining sufficient pure clutter auxiliary data in real-world environments, and the fact that radar receivers using centrosymmetric linear arrays or centrosymmetric interval pulse trains exhibit a special oblique-symmetric structure in their clutter covariance matrix, utilizing this oblique-symmetric structure information can often improve the detector's detection performance and reduce the need for auxiliary data.

[0004] In situations with external interference and limited uniform auxiliary data, multi-channel broadband radar range-extended target detection faces the challenge of balancing detection performance and mismatch robustness. A key challenge is how to fully utilize oblique symmetric structural information, reduce the actual demand for auxiliary data, improve the estimation accuracy of the unknown clutter covariance matrix, and construct a closed-form range-extended target robust intelligent detection method. This method effectively suppresses interference signals while maintaining constant false alarm rate (CFAR) characteristics, achieving an effective balance between mismatch robustness and detection performance. This is crucial for enhancing the detection capabilities of broadband radar in complex interference environments and is one of the challenges faced by adaptive range-extended target detection in multi-channel broadband radar. Summary of the Invention

[0005] To overcome the problems in the prior art, this invention proposes an adaptive target detection method with quasi-whitening and intelligent interference suppression.

[0006] The technical solution of the present invention to solve the above-mentioned technical problems is as follows:

[0007] A target adaptive detection method for quasi-whitening and intelligent interference suppression, comprising the following steps:

[0008] Step 1. Obtain the main data Z from K to-be-detected range cells, and perform a skew-symmetric unitary transformation on the main data Z to obtain the skew-symmetric transformed main data Under the condition of a known clutter skew-symmetric covariance matrix, use the complex Gaussian probability density function of the skew-symmetric transformed main data to take the partial derivative with respect to the interference coordinate vector q and solve for the interference coordinate vector q t the maximum likelihood estimate under the no-target hypothesis Use the complex Gaussian probability density function of the skew-symmetric transformed main data under the target hypothesis to take the partial derivative with respect to the joint coordinate vector q of the target signal and interference t and solve for d t the maximum likelihood estimate under the target hypothesis Then, combined with the integration result of the texture component τ, construct a range extended target GLRT detection statistic under the condition of a known clutter skew-symmetric covariance matrix;

[0009] Step 2. Obtain the auxiliary data Y from R reference range cells adjacent to the to-be-detected range cells, and perform a skew-symmetric unitary transformation on the auxiliary data Y to obtain the skew-symmetric transformed auxiliary data Use the complex Gaussian probability density function of the skew-symmetric transformed auxiliary data to take the derivative with respect to the clutter skew-symmetric covariance matrix and set it to zero, obtain the maximum likelihood estimate of the clutter skew-symmetric covariance matrix based on the auxiliary data, substitute the maximum likelihood estimate of the clutter skew-symmetric covariance matrix into the range extended target GLRT detection statistic obtained in Step 1, replace the unknown clutter skew-symmetric covariance matrix therein, and construct the detection statistic λ of the target adaptive detection method for quasi-whitening and intelligent interference suppression;

[0010] Step 3. Set the detection threshold T according to the preset false alarm probability; compare the detection statistic λ with the detection threshold T. If λ≥T, it is determined that there is a range extended target in the current to-be-detected range cell, and the main data is not used as the auxiliary data for subsequent other to-be-detected range cells; otherwise, if λ<T, it is determined that there is no range extended target in the current to-be-detected range cell, and the main data is used as the auxiliary data for subsequent other to-be-detected range cells.

[0011] Further, in Step 1, under the condition of a known clutter skew-symmetric covariance matrix, use the complex Gaussian probability density function of the skew-symmetric transformed main data to take the partial derivative with respect to the interference coordinate vector q t and solve for the interference coordinate vector q t the maximum likelihood estimate under the no-target hypothesis

[0012]

[0013] in,

[0014]

[0015]

[0016] In the formula,

[0017]

[0018]

[0019]

[0020] The master data is represented as an N×K dimensional complex matrix Z = [z1, z2, ..., z K Assuming the target may occupy K consecutive detection range cells, under assumption H1, the N×1 dimensional received complex signal in the t-th detection range cell is represented as z. t =s t +j t +c t (t=1,2,...,K), where the N×1 dimensional target complex signal vector s t and N×1 dimensional interference complex vector j t All are assumed to be deterministic and can be represented as s respectively. t =Hp t and j t =Jq t H and J are the known full-rank N×p-dimensional target signal subspace complex matrix and N×q-dimensional interference signal subspace complex matrix, respectively, and p×1-dimensional complex vector p. t and q×1 dimensional complex vector q t Represent the unknown complex coordinate vectors of the target signal and the interference signal, respectively; N×1 dimensional clutter vectors in t range cells to be detected. Represents the clutter signal vector; g t It is a zero-mean complex Gaussian vector with covariance matrix M, representing the speckle component of local backscattering; τ t It is a non-negative random variable representing the texture component of local reflection power variation;

[0021] The oblique-symmetric unitary transformation matrix T can be expressed as:

[0022]

[0023] In the above formula, I m Represents an m×m dimensional unit matrix, with imaginary units. Matrix D m It is an m×m dimensional skew-symmetric matrix.

[0024] Furthermore, in step 1, the master data is transformed using a skew-symmetric method under the objective assumption. The complex Gaussian probability density function for the joint coordinate vector q of the target signal and interference t Find the partial derivative and solve for d. t Maximum likelihood estimation under objective assumptions

[0025]

[0026] in,

[0027]

[0028] In the formula,

[0029] Furthermore, in step 1, a range-extended target GLRT detection statistic is constructed under the condition of a known clutter oblique-symmetric covariance matrix:

[0030]

[0031] In the formula, β t Indicates the proportional parameter.

[0032] Furthermore, in step 2, fixed-point estimation is used to obtain an estimate of the clutter oblique-symmetric covariance matrix M:

[0033]

[0034] in,

[0035]

[0036] In the formula, and Let y represent the i-th and i+1-th iteration estimates of the clutter covariance matrix M, respectively; y represents the observed data. t (t=1,2,...,R) contains pure clutter components, and the N×1 dimensional complex vector y of the t-th reference range cell. t (t=1,2,...,R) satisfies They are also independently and identically distributed across different distance units.

[0037] Furthermore, in step 2, the maximum likelihood estimate of the clutter oblique symmetric covariance matrix is ​​substituted into the range-extended target GLRT detection statistic obtained in step 1, replacing the unknown clutter oblique symmetric covariance matrix, to construct the detection statistic λ of the target adaptive detection method for quasi-whitening and intelligent interference suppression:

[0038]

[0039] in,

[0040]

[0041] Compared with the prior art, the present invention has the following technical effects:

[0042] 1) By fully utilizing the skew symmetry information of the clutter covariance matrix, the estimation accuracy of the unknown clutter covariance matrix is ​​improved, the requirement for auxiliary data is reduced, and strong support is provided for the adaptive detection of range-extended targets under small sample conditions where interference exists.

[0043] 2) A two-step GLRT oblique-symmetric intelligent robust detection method for range-extended targets was constructed. Its detector has a closed-form expression and low computational complexity, making it easy to implement in engineering.

[0044] 3) For interference environments with subspace structure, the two-step GLRT oblique symmetric intelligent robust detection method for range extension targets of the present invention can effectively suppress interference signals of different intensities and has good intelligent anti-interference performance.

[0045] 4) In the case of target signal steering vector mismatch, the two-step GLRT oblique symmetric intelligent robust detection method of the present invention can effectively detect mismatch signals and has strong detection robustness for mismatch signals;

[0046] 5) The detection method of the present invention maintains the characteristics of CFAR while balancing the performance of algorithm computational complexity, detection performance and mismatch robustness, and improves the adaptive detection performance of multi-channel broadband radar for weak and mismatched targets in complex environments.

[0047] 6) The method of the present invention is applicable to some non-wideband radar detection situations, such as using low / medium resolution radar to detect large targets or to detect groups of spatially adjacent point targets moving at the same speed (such as ship formations, aircraft formations, vehicle formations, etc.), and has good application prospects. Attached Figure Description

[0048] Figure 1 This is a functional block diagram of the target adaptive detection method for quasi-whitening and intelligent interference suppression of the present invention;

[0049] Figure 2 This is a comparison chart of the detection performance of the method of the present invention and existing detection methods on the matching signal;

[0050] Figure 3 This is a comparison chart of the detection performance of the method of the present invention and existing detection methods for mismatch signals;

[0051] Figure 2 In the given conditions, N=12, K=15, R=12, 24, p=3, q=2, the false alarm probability P0 fa =10 -3 Interference clutter power ratio (ICR) = 15 dB;

[0052] Figure 3 In the given information, N = 12, K = 15, R = 12, 24, p = 3, q ​​= 2, P fa =10 -3 ICR = 15dB, mismatch angle squared value cos 2 φ = 0.5. Detailed Implementation

[0053] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0054] To address the challenges of existing wideband radar range-extended target adaptive detectors (REEDs) in complex Gaussian backgrounds, which struggle to balance computational complexity, CFAR characteristics, detection performance, and mismatch robustness, and considering the difficulty in obtaining pure clutter auxiliary data due to actual clutter inhomogeneity, this paper proposes a target adaptive detection method with closed-form quasi-whitening and intelligent interference suppression. This method aims to fully exploit the clutter covariance matrix structure information, further reduce the need for auxiliary data, improve the estimation accuracy of unknown clutter covariance matrices, and construct a target adaptive detection method with closed-form quasi-whitening and intelligent interference suppression. While ensuring CFAR characteristics, this method also addresses the computational complexity, intelligent anti-interference, mismatch robustness, and detection performance requirements of range-extended target adaptive detection algorithms, thereby improving the adaptive detection performance of multi-channel wideband radars for weak and mismatched targets in complex interference environments.

[0055] The target adaptive detection method of the present invention, which combines quasi-whitening and intelligent interference suppression, includes the following steps:

[0056] Step 1. Obtain master data Z from K range cells to be detected, and perform a skew-symmetric unitary transform on the master data Z to obtain skew-symmetric transformed master data. Given the clutter oblique-symmetric covariance matrix, the master data is transformed using oblique-symmetric transformation under the no-target assumption. The complex Gaussian probability density function for the interference coordinate vector q t Find the partial derivative and solve for the interference coordinate vector q. t Maximum likelihood estimation under no objective assumption Using oblique symmetric transformation of master data under the objective assumption The complex Gaussian probability density function for the joint coordinate vector q of the target signal and interference t Find the partial derivative and solve for d. t Maximum likelihood estimation under objective assumptions Then, by combining the integral results of the texture component τ, a range-extended target GLRT detection statistic is constructed under the condition of known clutter oblique symmetric covariance matrix.

[0057] The specific steps include:

[0058] For a coherent radar system with N joint space-time channels, consider the binary hypothesis testing problem of H0 and H1. Under hypothesis H0, the target does not exist, and only pure clutter exists; under hypothesis H1, the target, clutter, and interference all exist. Assuming the target may occupy K consecutive range cells to be detected, under hypothesis H1, the N×1 dimensional received complex signal in the t-th range cell is represented as z. t =s t +j t +c t (t=1,2,...,K), where the N×1 dimensional target complex signal vector s t and N×1 dimensional interference complex vector j t All are assumed to be deterministic and can be represented as s respectively. t =Hp t and j t =Jq t H and J are the known full-rank N×p-dimensional target signal subspace complex matrix and N×q-dimensional interference signal subspace complex matrix, respectively, and p×1-dimensional complex vector p. t and q×1 dimensional complex vector q t The unknown complex coordinate vectors representing the target signal and the interference signal are respectively represented by the main data, which can be represented as an N×K dimensional complex matrix Z = [z1, z2, ..., z...]. K Note that subspaces H and J are linearly independent. Construct a full-rank augmented matrix B = [HJ], satisfying p + q ≤ N. The N×1 clutter vector in the t-th range cell to be detected. Represents the clutter signal vector; g t It is a zero-mean complex Gaussian vector with covariance matrix M, representing the speckle component of local backscattering; τ t Let be a non-negative random variable representing the texture component with varying local reflection power. Furthermore, assume the texture component τ... t Following an inverse gamma distribution, τ t The probability density function is given by the following equation:

[0059]

[0060] Where Γ(·) is the gamma function; the nonnegative constant α t and β tLet H, J, and M represent the shape and scale parameters, respectively, and assume they are known or can be accurately estimated. Furthermore, when the radar receiving system employs a centrally symmetric linear array or a centrally symmetric interval pulse train, H, J, and M all possess oblique symmetric structures. Based on the oblique symmetric structure information in the received data, the complex matrices H, J, and M can be converted into real matrices respectively using an oblique symmetric unitary transformation T. and Simultaneously, the original master data z can be transformed using a skew-symmetric unitary transformation. t and auxiliary data y t Perform the transformation, that is

[0061]

[0062] in, and Let T represent the set of m×n dimensional real matrices and the set of complex matrices, respectively; the skew-symmetric unitary transformation matrix T can be represented as...

[0063]

[0064] In the above formula, I m Represents an m×m dimensional unit matrix, with imaginary units. D is an m×m dimensional skew-symmetric matrix m Represented as

[0065]

[0066] Assuming clutter covariance matrix It is known that the detection statistic based on the GLRT test criterion can be expressed as:

[0067]

[0068] Where P = [p1,...,p K Q = [q1,...,q] K ],τ=[τ1,τ2,…,τ K ]; and These represent the test data under hypotheses H0 and H1, respectively. The probability density function (PDF) of can be expressed as

[0069]

[0070] in, d t =[p t T q t T ] T .

[0071] Next, we will try to obtain q under hypothesis H0. t ML estimation (represented as) ) and under hypothesis H1 d t ML estimation (represented as) It is easy to obtain by taking partial derivatives.

[0072]

[0073]

[0074] in, Substituting (6) and (7) into (5), we can obtain

[0075]

[0076]

[0077] Next, multiply (9) and (10) by (1) respectively, then integrate over τ, and substitute the result into (5) to obtain

[0078]

[0079] Due to α t It is certain and known or can be accurately estimated, therefore (11) can be rewritten as

[0080]

[0081] Step 2. Obtain auxiliary data Y from R reference range cells adjacent to the range cell to be detected, and perform a skew-symmetric unitary transform on the auxiliary data Y to obtain skew-symmetric transform auxiliary data. Using skew-symmetric transformation to assist data The derivative of the complex Gaussian probability density function with respect to the clutter oblique symmetric covariance matrix is ​​taken and set to zero to obtain the maximum likelihood estimate of the clutter oblique symmetric covariance matrix based on auxiliary data. The maximum likelihood estimate of the clutter oblique symmetric covariance matrix is ​​then substituted into the range-extended target GLRT detection statistic obtained in step 1, and the unknown clutter oblique symmetric covariance matrix is ​​replaced to construct the detection statistic λ of the target adaptive detection method for quasi-whitening and intelligent interference suppression.

[0082] The specific steps include:

[0083] To estimate the clutter covariance matrix, R observation data y are obtained from R reference range cells adjacent to the range cell to be detected. t (t = 1, 2, ..., R), assume y t If (t=1,2,...,R) contains only pure clutter components, then the auxiliary data can be represented as an N×R dimensional complex matrix Y=[y1,y2,...,yR ], where the N×1 dimensional complex vector y of the t-th reference distance unit. t (t=1,2,...,R) satisfies They are also independently and identically distributed across different distance units.

[0084] The original auxiliary data Y = [y1, y2, ..., y] is transformed by a skew-symmetric unitary transformation T. R By performing the transformation, we can obtain the auxiliary data for the oblique symmetric transformation.

[0085]

[0086] Fixed-point (FP) estimation is widely used for estimating the covariance matrix in complex Gaussian clutter. FP estimation can be expressed as...

[0087]

[0088] in and Let represent the i-th and i+1-th iteration estimates of the clutter covariance matrix M, respectively. The initial values ​​of M are set to... The number of iterations is n. This section considers oblique-symmetric prior information and uses fixed-point estimation to obtain an estimate of M, i.e.

[0089]

[0090] Then, using Replace the unknown matrix in (12) The detection statistics of the target adaptive detector with quasi-whitening and intelligent interference suppression can be obtained as follows:

[0091]

[0092] in

[0093]

[0094] Note that existing range extension target detectors such as S-GLRT do not consider oblique symmetry structural information and require R≥N. The detection method of this invention reduces the need for auxiliary data and provides strong support for adaptive detection of range extension targets under small sample conditions with interference.

[0095] The method of the present invention constructs a two-step GLRT skew-symmetric intelligent robust detector for range-extended targets. As can be seen from Equation (17), the proposed target adaptive detection method with quasi-whitening and intelligent interference suppression has a closed-form expression for the detection statistic and does not require iterative operations. Additionally, it is worth noting that compared with the S-GLRT detector for range-extended targets, the target adaptive detection method with quasi-whitening and intelligent interference suppression has a lower algorithm computational complexity and stronger detection robustness against steering vector mismatch signals. Generally speaking, the target adaptive detection method with quasi-whitening and intelligent interference suppression of the present invention can effectively balance the algorithm computational complexity, mismatch robustness, and detection performance while maintaining the CFAR characteristic.

[0096] Step 3. To maintain the CFAR characteristic of the detection method, set the detection threshold T according to the preset false alarm probability; compare the detection statistic λ with the detection threshold T. If λ≥T, it is determined that there is a range-extended target in the current range cell to be detected, and the primary data is not used as the auxiliary data for other subsequent range cells to be detected; otherwise, if λ<T, it is determined that there is no range-extended target in the current range cell to be detected, and the primary data is used as the auxiliary data for other subsequent range cells to be detected.

[0097] To verify the effectiveness of the method of the present invention, two embodiments are given in this specific implementation manner. The first embodiment is for the sea detection environment, and the second embodiment is for the land detection environment.

[0098] Embodiment 1:

[0099] Refer to the attached drawings of the specification Figure 1 , and the specific implementation manner of Embodiment 1 is divided into the following steps:

[0100] Step A1. Use a sea detection radar to irradiate the sea area to be detected, obtain the primary data Z of K range cells to be detected, and send the primary data Z to the unitary transformation module; in the unitary transformation module, obtain the skew-symmetric transformation primary data according to Equation (2) Send the skew-symmetric transformation primary data to the maximum likelihood estimation solving module under H0 hypothesis and the maximum likelihood estimation solving module under H1 hypothesis; in the maximum likelihood estimation solving module under H0 hypothesis, obtain the maximum likelihood estimation of q t under H0 hypothesis according to Equation (7) In the maximum likelihood estimation solving module under H1 hypothesis, obtain the maximum likelihood estimation of d t under H1 hypothesis according to Equation (8) The results obtained from the maximum likelihood estimation solution module under the H0 assumption and the maximum likelihood estimation solution module under the H1 assumption are sent to the GLRT detection statistic construction module under the known covariance matrix condition. Based on Equation (12), the range extension target GLRT detection statistic under the known clutter oblique symmetric covariance matrix condition is constructed and sent to the target adaptive detector construction module for quasi-whitening and intelligent interference suppression.

[0101] It is worth noting that in step A1, the sea clutter component is modeled using a complex Gaussian distribution. Considering that external interference in the actual marine environment may adversely affect the adaptive detection of range-extended targets, external interference is also taken into account during the detector design process. Subspace signals are used to model the interference to reduce the potential mismatch effect of the interference signal. For interference environments with subspace structure, the target adaptive detection method of this invention, which combines quasi-whitening and intelligent interference suppression, can effectively suppress interference signals of different intensities, exhibiting good intelligent anti-interference capabilities. Regarding target signal steering vector mismatch, the target adaptive detection method of this invention, which combines quasi-whitening and intelligent interference suppression, can effectively detect mismatched signals and has strong robustness in detecting mismatched signals.

[0102] Step A2. Illuminate the targetless area around the sea area to be detected with radar to obtain auxiliary data Y containing only pure sea clutter in R reference range cells; send the auxiliary data Y to the unitary transform module; in the unitary transform module, perform a skew-symmetric unitary transform on Y according to equation (2) to obtain skew-symmetric transform auxiliary data. Oblique symmetric transformation auxiliary data The data is sent to the maximum likelihood estimation module for the oblique-symmetric clutter covariance matrix, and the maximum likelihood estimate of the clutter oblique-symmetric covariance matrix based on auxiliary data is obtained according to equation (14-15). Will The target adaptive detector construction module, which is sent to the quasi-whitening and interference intelligent suppression module, will... Substitute the range-extended target GLRT detection statistics obtained in step A1 into the unknown clutter oblique symmetric covariance matrix, construct the detection statistics λ of the target adaptive detection method with quasi-whitening and intelligent interference suppression according to equation (16), and send λ to the detection decision module.

[0103] It should be noted that in step A2, the maximum likelihood estimation of the clutter skew-symmetric covariance matrix utilizes the skew-symmetric structure information, improving the estimation accuracy of the unknown sea clutter covariance matrix. Compared with the existing clutter covariance matrix estimation methods that do not consider the skew-symmetric structure information, the covariance matrix maximum likelihood estimation constructed by the method of the present invention considering the skew-symmetric structure information reduces the requirement for the amount of pure sea clutter auxiliary data, providing strong support for the adaptive detection of range-extended targets under the condition of small sample sea clutter with interference. Compared with the S-GLRT detector for range-extended targets, the algorithm of the method of the present invention has lower computational complexity and stronger detection robustness to steering vector mismatch signals. In addition, the constructed target adaptive detection method of quasi-whitening and interference intelligent suppression has a closed-form expression. Compared with the existing range-extended target adaptive detection methods, while maintaining the CFAR characteristics, it takes into account the performance balance of algorithm computational complexity, detection performance, and mismatch robustness, enhancing the adaptive detection ability of multi-channel broadband radar for weak targets and mismatch targets on the sea surface in complex electromagnetic environments.

[0104] Step A3. Set the detection threshold T according to the preset false alarm probability: Specifically, set the false alarm probability as P fa , according to the Monte Carlo method, calculate the detection threshold T based on 100 / P measured sea clutter data accumulated in the early stage fa pieces. Considering the difficulty in obtaining sea clutter, if the amount R of actually obtained pure sea clutter measured data is less than 100 / P fa , then the missing 100 / P fa - R clutter data can be obtained by simulating with a sea clutter simulation model, and the model parameters are reasonably estimated and set according to the already obtained pure sea clutter measured data. Further, compare the detection statistic λ with the detection threshold T. If λ≥T, it is determined that there are range-extended targets in the current K to-be-detected range cells, and the main data is not used as the auxiliary data for other subsequent to-be-detected range cells; otherwise, if λ<T, it is determined that there are no range-extended targets in the current K to-be-detected range cells, and the main data is used as the auxiliary data for other subsequent to-be-detected range cells.

[0105] The comparison results of the detector performance in the target steering vector matching environment are shown in the appendix Figure 2 . The results show that compared with the existing S-GLRT detector for range-extended targets, the detector of the method of the present invention has better detection performance in the matching environment.

[0106] Example 2:

[0107] Refer to the appendix of the specification Figure 1 , and the specific implementation of Example 2 is divided into the following steps:

[0108] Step B1. Using the ground detection radar, illuminate the area to be detected to obtain the master data Z of K range cells to be detected, and send the master data Z to the unitary transform module; in the unitary transform module, obtain the oblique symmetric transform master data according to equation (2). Oblique symmetric transformation of master data The results are sent to the maximum likelihood estimation solution modules under the H0 assumption and the H1 assumption; in the maximum likelihood estimation solution module under the H0 assumption, q under the H0 assumption is obtained according to equation (7). t Maximum likelihood estimation In the maximum likelihood estimation solution module under the H1 assumption, d under the H1 assumption is obtained according to equation (8). t Maximum likelihood estimation The results obtained from the maximum likelihood estimation solution module under the H0 assumption and the maximum likelihood estimation solution module under the H1 assumption are sent to the GLRT detection statistic construction module under the known covariance matrix condition. Based on Equation (12), the range-extended target GLRT detection statistic under the known clutter oblique symmetric covariance matrix condition is constructed and sent to the target adaptive detector construction module for quasi-whitening and intelligent interference suppression.

[0109] It is worth noting that in step B1, the ground clutter component is modeled using a complex Gaussian distribution. Considering that external interference in the actual ground environment may adversely affect the adaptive detection of range-extended targets, external interference is also taken into account during the detector design process. Subspace signals are used to model the interference to reduce the potential mismatch effects of the interference signal. For interference environments with subspace structure, the target adaptive detection method of this invention, which combines quasi-whitening and intelligent interference suppression, can effectively suppress interference signals of different intensities, exhibiting good intelligent anti-interference capabilities. Regarding target signal steering vector mismatch, the target adaptive detection method of this invention, which combines quasi-whitening and intelligent interference suppression, can effectively detect mismatched signals and has strong robustness in detecting mismatched signals.

[0110] Step B2. Illuminate the targetless area around the detection area with radar to obtain auxiliary data Y containing only sea clutter in R reference range cells; send the auxiliary data Y to the unitary transform module; in the unitary transform module, perform a skew-symmetric unitary transform on Y according to equation (2) to obtain skew-symmetric transform auxiliary data. Oblique symmetric transformation auxiliary data The data is sent to the maximum likelihood estimation module for the oblique-symmetric clutter covariance matrix, and the maximum likelihood estimate of the clutter oblique-symmetric covariance matrix based on auxiliary data is obtained according to equation (14-15). Will The target adaptive detector construction module, which is sent to the quasi-whitening and interference intelligent suppression module, will... Bring in the distance-extension target GLRT detection statistic obtained in step A1, replace the unknown clutter skew-symmetric covariance matrix therein, construct the detection statistic λ of the target adaptive detection method for quasi-whitening and interference intelligent suppression according to Equation (16), and send λ to the detection decision module.

[0111] It should be noted that in step B2, the maximum likelihood estimation of the clutter skew-symmetric covariance matrix utilizes the skew-symmetric structure information, improving the estimation accuracy of the unknown ground clutter covariance matrix; compared with the existing clutter covariance matrix estimation method that does not consider the skew-symmetric structure information, the covariance matrix maximum likelihood estimation constructed by the method of the present invention considering the skew-symmetric structure information reduces the requirement for the amount of pure ground clutter auxiliary data, providing strong support for realizing the adaptive detection of distance-extension targets under the condition of small sample local clutter with interference. Compared with the S-GLRT detector of distance-extension targets, the algorithm of the method of the present invention has lower computational complexity and stronger detection robustness for steering vector mismatch signals. In addition, the constructed target adaptive detection method for quasi-whitening and interference intelligent suppression has a closed-form expression. Compared with the existing distance-extension target adaptive detection methods, while maintaining the CFAR characteristics, it takes into account the performance balance of algorithm computational complexity, detection performance, and mismatch robustness, improving the adaptive detection ability of multi-channel broadband radars for weak ground targets and mismatch targets in complex electromagnetic environments.

[0112] Step B3. Set the detection threshold T according to the preset false alarm probability: specifically, set the false alarm probability to P fa , and according to the Monte Carlo method, calculate the detection threshold T based on 100 / P fa measured ground clutter data accumulated previously. Further, compare the detection statistic λ with the detection threshold T. If λ≥T, it is determined that there are distance-extension targets in the current K to-be-detected distance units, and the main data is not used as the auxiliary data for other subsequent to-be-detected distance units; otherwise, if λ<T, it is determined that there are no distance-extension targets in the current K to-be-detected distance units, and the main data is used as the auxiliary data for other subsequent to-be-detected distance units.

[0113] The comparison results of the detector performance in the environment of target steering vector mismatch are shown in the appendix Figure 3 . The results show that compared with the existing S-GLRT detector for distance-extension targets, the detector of the method of the present invention has better detection robustness in the mismatch environment.

[0114] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A target adaptive detection method with quasi-whitening and intelligent interference suppression, characterized in that, Includes the following steps: Step 1. From K Each distance unit to be detected acquires master data. Z For master data Z Obtain the main data of the oblique symmetric transformation by performing an oblique symmetric unitary transformation. Given the clutter oblique-symmetric covariance matrix, the master data is transformed using oblique-symmetric transformation under the no-target assumption. The complex Gaussian probability density function for the disturbance coordinate vector Find the partial derivative and solve for the interference coordinate vector. Maximum likelihood estimation under no objective assumption ;Utilizing the objective assumption of skew-symmetric transformation of master data The complex Gaussian probability density function for the joint coordinate vector of the target signal and interference Find the partial derivative and solve. Maximum likelihood estimation under objective assumptions Then combine the texture components Based on the integral results, a range-extended target GLRT detection statistic is constructed under the condition of known clutter oblique symmetric covariance matrix; Step 2. From the nearest distance unit to be detected R Auxiliary data is obtained from each reference distance cell. Y For auxiliary data Y Obtain auxiliary data for oblique symmetric transformation by performing oblique symmetric unitary transformation. Using skew-symmetric transformation to assist data The derivative of the complex Gaussian probability density function with respect to the clutter oblique symmetric covariance matrix is ​​taken and set to zero to obtain the maximum likelihood estimate of the clutter oblique symmetric covariance matrix based on auxiliary data. This maximum likelihood estimate is then substituted into the range-extended target GLRT detection statistic obtained in step 1, replacing the unknown clutter oblique symmetric covariance matrix, to construct the detection statistic for the target adaptive detection method with quasi-whitening and intelligent interference suppression. ; Step 3. Set the detection threshold according to the preset false alarm probability. T ; Detection statistics With detection threshold T If a comparison is made, If the current detection range cell contains a range extension target, the master data will not be used as auxiliary data for other subsequent detection range cells; otherwise... If no target is found in the current detection range cell, the master data is used as auxiliary data for other detection range cells.

2. The target adaptive detection method for quasi-whitening and intelligent interference suppression according to claim 1, characterized in that, In step 1, given the clutter oblique-symmetric covariance matrix, the master data is transformed using oblique-symmetric transformation without the target assumption. The complex Gaussian probability density function for the disturbance coordinate vector Find the partial derivative and solve for the interference coordinate vector. Maximum likelihood estimation under no objective assumption : in, In the formula, Master data is represented as 3D complex matrix Assuming the target may occupy a continuous range K The nth distance cell to be detected, under assumption H1, the nth t In each distance unit to be detected The received complex signal is represented as ,in 3D target complex signal vector and 3D interference complex vector All are assumed to be deterministic, and are respectively expressed as and , and Each column has a known full rank. The complex matrix of the target signal subspace and the sum of the dimensions 3D interference signal subspace complex matrix, 3D complex vector p t and 3D complex vector q t These represent the unknown complex coordinate vectors of the target signal and the interference signal, respectively; t In each distance unit to be detected dimensional clutter vector Represents the clutter signal vector; It has a covariance matrix M The zero-mean complex Gaussian vector represents the speckle component of local backscattering; It is a non-negative random variable representing the texture component of local reflection power variation; Oblique symmetric unitary transformation matrix T Represented as: In the above formula express 3D unit matrix, imaginary unit ,matrix for A 2D skew-symmetric matrix.

3. The target adaptive detection method for quasi-whitening and intelligent interference suppression according to claim 2, characterized in that, In step 1, the master data is transformed using a skew-symmetric method under the objective assumption. The complex Gaussian probability density function for the joint coordinate vector of the target signal and interference Find the partial derivative and solve. Maximum likelihood estimation under objective assumptions : in, In the formula, , .

4. The target adaptive detection method for quasi-whitening and intelligent interference suppression according to claim 3, characterized in that, In step 1, the range-extended target GLRT detection statistics are constructed under the condition of known clutter oblique-symmetric covariance matrix: In the formula, Indicates the proportional parameter. , .

5. The target adaptive detection method for quasi-whitening and intelligent interference suppression according to claim 4, characterized in that, In step 2, fixed-point estimation is used to obtain the clutter oblique-symmetric covariance matrix. M Estimate: in, In the formula, and Let them represent the clutter covariance matrix respectively. M The i and i +1 iteration estimation; observation data Contains pure clutter components, the first t Reference distance cells 3D complex vector satisfy They are also independently and identically distributed across different distance units.

6. The target adaptive detection method for quasi-whitening and intelligent interference suppression according to claim 5, characterized in that, In step 2, the maximum likelihood estimate of the clutter oblique symmetric covariance matrix is ​​substituted into the range-extended target GLRT detection statistics obtained in step 1, replacing the unknown clutter oblique symmetric covariance matrix, to construct the detection statistics for the target adaptive detection method of quasi-whitening and intelligent interference suppression. : in, .

Images