compound-Gaussian Clutter Research Articles

Adaptive polarimetric persymmetric detection for distributed subspace targets in lognormal texture clutter

In this paper, the adaptive polarimetric persymmetric detection for distributed subspace targets under the background of compound Gaussian clutter is investigated, where the compound Gaussian clutter exhibits texture that follows a lognormal distribution. Based on the two-step Generalized Likelihood Ratio Test (2S GLRT), two-step maximum a posteriori Generalized Likelihood Ratio Test (2S MAP GLRT), two-step Rao (2S Rao) test and two-step Wald (2S Wald) test, we have proposed four polarimetric persymmetric detectors. Initially, we model the target echo as a distributed subspace signal, assuming known clutter texture and polarization speckle covariance matrix (PSCM), and derive the corresponding test statistics. Then, the estimation of the lognormal texture is obtained through maximum a posteriori (MAP). Conventionally, a set of secondary data, which share the same PSCM as the cells under test (CUTs), is assumed to participate in the estimation of the PSCM, leveraging its inherent persymmetric property during the estimation process. Finally, the estimated values are substituted into the proposed test statistics to obtain fully adaptive polarimetric persymmetric detectors. Numerical experimental results using simulated data and measured sea clutter data demonstrate that the proposed four adaptive polarimetric persymmetric detectors exhibit a constant false alarm rate (CFAR) characteristic relative to the PSCM and satisfactory detection performance for distributed subspace targets.

Fractional Negative Order Moment Parameter Estimator of Compound-Gaussian Clutter with Lognormal Texture

Fractional Negative Order Moment Parameter Estimator of Compound-Gaussian Clutter with Lognormal Texture

Mixture texture model with weighted generalized inverse Gaussian distribution for target detection

With the improvement of radar resolution, the amplitude distribution of sea clutter has begun to exhibit significant heavy-tailed characteristics. Existing models for sea clutter amplitude distribution do not sufficiently solve this problem, which results in a diminished target detection probability in two-step generalized likelihood ratio test (GLRT) based target detectors. To address this issue and to enhance the radar target detection capabilities, we propose a new compound-Gaussian clutter model based on a weighted mixture of generalized inverse Gaussian distributions to model the texture. The CG-WGIG model demonstrates greater flexibility and effectively captures the characteristics of sea clutter amplitude distributions with heavy tails, thus affording a better fit. We combine this model with the two-step GLRT criterion to propose a GLRT detector endowed with a weighted generalized inverse Gaussian texture (GLRT-WGIG), specifically tailored for detecting range-spread targets (RSTs). We have conducted a rigorous mathematical proof to demonstrate that the constant false alarm rate (CFAR) characteristic of the proposed GLRT-WGIG detector is independent of the covariance matrix, thereby ensuring its robustness in various operational scenarios. Using measured data, we demonstrate that the proposed model and detector are flexible and outperform the existing methods in terms of fitting and detection performance, where we obtain a mean 12.02% MSE of fitting performance promotion.

Analytical Coherent Detection in High-Resolution Dual-Polarimetric Sea Clutter with Independent Inverse Gamma Textures

Polarization diversity has been widely used in maritime radars to improve target detection performance. Full utilization of the polarimetric characteristics of sea clutter is the key to designing effective polarimetric detectors. For high-resolution maritime radars, the HH-HV dual-polarization is an affordable and effective mode to monitor small targets, owing to the simple configuration of single-polarimetric transmit and dual-polarimetric reception and lower clutter powers at the HH and HV polarizations. Enlightened by the analytical coherent detector in compound-Gaussian clutter with inverse Gamma texture, this paper investigates dual-polarimetric coherent detection in dual-polarimetric compound-Gaussian clutter with independent inverse Gamma distributed textures. The analytical dual-polarimetric near-optimum coherent detector is derived, which is a fusion of the generalized likelihood ratio test linear threshold detectors (GLRT-LTDs) at the two polarizations. For short, it is referred to as the P-GLRT-LTD. It is proven that the P-GLRT-LTD is of constant false alarm rate with respect to the Doppler steering vector, scale parameters of textures, and speckle covariance matrices. Moreover, the thresholds of the P-GLRT-LTD are given analytically. Experiments using simulated sea clutter data with the estimated scale and shape parameters from the two measured intelligent pixel processing radar (IPIX) datasets and two measured IPIX datasets with test targets are made to compare P-GLRT-LTD with other existing dual-polarimetric coherent detectors. The results show that the P-GLRT-LTD attains the same detection performance as the existing best-performance detector. The P-GLRT-LTD has a lower computational cost than the existing best-performing one.

Coherent Detection of Weak Moving Targets in Compound-Gaussian Clutter Using Nonlinear Preprocessing System: Performance Measure and Implementation

Coherent Detection of Weak Moving Targets in Compound-Gaussian Clutter Using Nonlinear Preprocessing System: Performance Measure and Implementation

Subspace-based distributed target detection in compound-Gaussian clutter

This paper investigates the problem of radar detection of distributed targets embedded in unknown compound-Gaussian clutter. To solve the problem, we first derive two detectors for fixed covariance matrix according to Rao and Wald tests with the use of order statistics theory. Then, the unknown covariance matrix is estimated by the approximate maximum likelihood (AML) estimation. Numerical examples through simulated and real data show that the Wald test-based detector has a higher probability of detection (PD) than existing detectors.

Bayesian Detectors for Wideband Radar Target With Range Walking in Compound Gaussian Clutter

In this paper, the problem of the wideband radar target detection with the range walking in compound Gaussian clutter without the secondary data is addressed. The wideband radar target return with the range walking is represented as a canonical linear model in the multi-rank subspace, and the clutter is modeled as a two-dimensional random process with separable complex inverse Wishart distributed random covariance matrices in the pulse and range-frequency domains. The generalized likelihood ratio test (GLRT), the Wald test and the Rao test based on the above target and clutter models are designed under the Bayesian framework. The Laplace approximation is adopted to compute the second-kind confluent hypergeometric function of matrix argument (SKCHFMA) in the proposed detectors. Finally, the performance of the detectors is validated by the simulations.

Range-Spread Target Detection Based on Adaptive Scattering Centers Estimation

Proper prior knowledge of target scattering centers (SCs) can help to obtain better detection performance of range-spread targets. However, target SCs are sensitive to the target’s attitude relative to the radar and vary significantly among different targets. The existing approaches that employ predetermined prior knowledge may suffer performance degradation when the prior information does not match the practical scenarios. A possible way to circumvent this drawback is to estimate the SCs of different targets adaptively and check the presence of a target utilizing the range cells occupied by the most likely target SCs. For this reason, this article develops a generalized likelihood ratio test based on adaptive SCs estimation (ASCE-GLRT) for range-spread target detection in compound-Gaussian clutter. Under the assumption that the target SCs are sparse, we model the problem of SCs estimation as a sparse signal representation. Moreover, since the sparse assumption may not always be satisfied in practice, a modified sparsity regularization method is proposed to enhance the robustness of the estimation performance of targets with different scattering characteristics. A theoretical analysis shows that the proposed detector can achieve the constant false alarm rate (CFAR) property. The performance assessments conducted by numerical simulation and field tests confirm the effectiveness and robustness of the proposed detector.

Improved Moving Target Detection in Homogeneous and Heterogeneous Clutter With Limited Secondary Data Using Unit Circle Roots Constraints

This paper proves that the roots of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$z$</tex-math></inline-formula> -polynomial corresponding to the ideal adaptive matched filter (AMF) lie on the unit circle (UC). Motivated by the proof, we propose UC roots-constrained AMF (UCRC-AMF) in this work that enforces this fundamental UC roots property while maximizing the SINR to determine the detector polynomials. The UCRC algorithm exploits polynomial conjugate symmetry and applies it to the problem of radar moving target detection (MTD) in homogeneous and heterogeneous clutter. The algorithm splits the multidimensional AMF optimization problem into multiple 1-D AMF problems. Simulation studies show that the UCRC-based AMF for Gaussian clutter, as well as UCRC versions of NAMF and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\alpha$</tex-math></inline-formula> -AMF for compound Gaussian clutter significantly outperform existing state-of-the-art detection algorithms in the presence of limited secondary data although the performance gain is persistent for larger secondary data as well. This work will show that the combined enforcement of UC roots property on detector polynomials and complex-persymmetry of covariance matrices is most effective for superior detection performance. It is also shown that the proposed UCRC-AMF outperforms existing methods when there is a mismatch with respect to the true target velocity and ideal covariance matrix structure. Additionally, it will be demonstrated via simulation that the proposed UCRC-based detectors have approximate CFAR with respect to the unknown covariance matrix.

Optimum and Near-Optimum Coherent CFAR Detection of Radar Targets in Compound-Gaussian Clutter With Generalized Inverse Gaussian Texture

In this article, we propose optimum and near-optimum adaptive coherent detectors of radar targets in compound-Gaussian clutter with generalized inverse Gaussian texture (CG-GIG clutter). The target amplitude and the speckle covariance matrix are modeled as unknown quantities to be estimated. On the basis of the two-step generalized likelihood ratio test (GLRT) and the estimate of the speckle covariance matrix, the optimum coherent detector and its adaptive version are designed in this article. It is demonstrated that the proposed optimum coherent detector contains three common detectors, which are the optimum K detector, the generalized likelihood ratio test linear-threshold detector (GLRT-LTD), and the GLRT detector for compound-Gaussian clutter with inverse Gaussian texture. Due to the existence of the modified Bessel function depending on data, the optimum coherent detector is computationally unrealizable. Therefore, a near-optimum coherent detector with a new structure named $\alpha$ matched filter ($\alpha$-MF) detector and its adaptive version are proposed, which decreases the computational cost. The proposed near-optimum coherent detector contains two common detectors, the GLRT-LTD and the $\alpha$-MF detector in K-distributed clutter, and has a comparable detection performance of the near-optimum detector in compound-Gaussian clutter with inverse Gaussian texture, which was proposed before. Theoretical analysis and numerical experiments illustrate that the proposed two detectors for CG-GIG clutter have the constant false alarm ratio property relative to the estimate of the speckle covariance matrix and the Doppler steering vector. Moreover, the detection performance of the two coherent detectors are evaluated by the simulated and real data.

Weak moving target detection based on suprathreshold stochastic resonance in heavy‐tailed sea clutter

Detection of weak moving targets with a low signal-to-clutter ratio (SCR) in heavy-tailed non-Gaussian sea clutter is a challenge problem in radar signal processing. In this study, an easily implementable suboptimum detector in compound-Gaussian (CG) clutter is proposed based on suprathreshold stochastic resonance (SSR). The structure of the locally optimum coherent detector for weak target detection in CG clutter is first analysed and the authors proved that it can be implemented using a pre-processing system followed by the adaptive matched filter (AMF) used in Gaussian backgrounds. A suboptimum pre-processing system is designed using the SSR system consisting of a parallel array of quantisers, which overcomes the high computation cost caused by the calculation of the data-dependent threshold for optimum detectors. Its input–output characteristic can be adjusted by adding different quantiser noise at the input of the system, making it suitable to be used for different distributions of input clutter. The gain of the SSR pre-processing system is introduced and the parameter determination for quantiser noise is analysed. Experiments on both simulated clutter and real sea clutter data in the Council for Scientific and Industrial Research's database and Intelligent PIXel Grimsby database have shown that the proposed detector based on SSR could achieve robust detection performance and perform better than the existing methods.

Waveform Optimization for Multistatic Radar Imaging Using Mutual Information

This article addresses the problem of radar waveform design for imaging targets in a cluttered environment. A multistatic radar scenario is considered for sparse and random sensor positions. The target impulse response is modeled using the simulated frequency response of a buried metallic mine, and the clutter is modeled using the compound Gaussian (CG) distribution. We explore novel waveform design techniques with respect to the mutual information (MI) criterion based on the CG clutter. The first method presents a waveform that exploits the CG distribution of the scene reflectivity function when projected onto a sparse basis. This is compared to the second method, called the target-specific approach that uses knowledge of the target and clutter frequency responses to optimize a matched illumination waveform. In both cases, the Taguchi and particle swarm optimization solvers are employed for MI-based waveform design optimization. To validate and compare the effectiveness of the optimized waveforms, the resulting scene reflectivity function is estimated using the sparsity-driven regularization radar imaging method. Our experimental results demonstrate that both waveform optimization techniques result in significantly better image reconstruction performance than the traditional linear-frequency-modulated waveform, and that the target-specific approach additionally suppresses clutter information in the scene.

Adaptive detection of distributed target in the presence of signal mismatch in compound Gaussian clutter

This paper deals with adaptive detection of distributed targets in the presence of signal mismatch in compound Gaussian clutter. The texture is assumed random to match the non-Gaussian environment. To increase the capability of rejecting mismatched signals, a fictitious subspace signal is introduced under the null hypothesis. Under the above assumptions, we propose two adaptive selective detectors by using two-step generalized likelihood ratio test (GLRT) and maximum a-posteriori (MAP) GLRT design criteria. The two new detectors are demonstrated to achieve constant false alarm rate property with respect to the speckle covariance matrix. Numerical simulations indicate that the new detectors outperform the conventional one in terms of selectivity property.

A Novel Gamma Distributed Random Variable (RV) Generation Method for Clutter Simulation with Non-Integral Shape Parameters.

Sea clutter simulation is a well-known research endeavour in radar detector analysis and design, and many approaches to it have been proposed in recent years, among which zero memory non-linear (ZMNL) and spherically invariant random process (SIRP) are the most two widely used methods for compound Gaussian distribution. However, the shape parameter of the compound Gaussian clutter model cannot be a non-integer nor non-semi-integer in the ZMNL method, and the computational complexity of the SIRP method is very high because of the complex non-linear operation. Although some improved methods have been proposed to solve the problem, the fitting degree of these methods is not high because of the introduction of Beta distribution. To overcome these disadvantages, a novel Gamma distributed random variable (RV) generation method for clutter simulation is proposed in this paper. In our method, Gamma RV with non-integral or non-semi-integral shape parameters is generated directly by multiplying an integral-shape-parameter Gamma RV with a Beta RV whose parameters are larger than 0.5, thus avoiding the deviation of simulation of Beta RV. A large number of simulation experimental results show that the proposed method not only can be used in the clutter simulation with a non-integer or non-semi-integer shape parameter value, but also has higher fitting degree than the existing methods.

Covariance‐free non‐homogeneity STAP detector in compound Gaussian clutter based on robust statistics

Space-time adaptive processing (STAP) detects targets by computing adaptive weight vectors for each cell under test using its covariance matrix, as estimated from surrounding secondary cells. In this study, the non-homogeneity detector (NHD) excludes the anomalous secondary cells that adversely affect the detection performance. The existing robust NHDs require estimating the covariance matrix of each secondary cell, which hinders their implementation in modern radars with large-dimensional range cells. In this study, the authors propose a new low-complexity NHD that is suitable for highly correlated clutter environments with both Gaussian and non-Gaussian heavy-tailed distributions. The proposed detector, which is based on the projection depth function from the field of robust statistics, features a non-parametric and covariance-free test statistic. As a result, its computational complexity is much lower than that of current NHDs, such as the widely used normalised adaptive matched filter (NAMF) detector, especially for large-dimensional range cells. In Monte Carlo simulations with different clutter distributions and radar system configurations, the proposed detector shows comparable performance to that of NAMF. The low complexity and robust performance of the new detector make it particularly attractive for real-time applications.

Knowledge-based target detection in compound Gaussian clutter with inverse Gaussian texture

This paper addresses the problem of adaptive detection of radar targets embedded in heterogeneous compound-Gaussian clutter environments. Based on the Bayesian theory, a priori knowledge of clutter is utilized to improve detection performance. The clutter texture is modeled by the inverse Gaussian distribution to describe the heavy-tailed clutter. Furthermore, clutter's heterogeneity results in insufficient secondary data, and the inverse complex Wishart distribution is exploited to model the speckle covariance matrix. Based on a priori distributions of clutter, a novel detector without using secondary data is derived via the generalized likelihood ratio test (GLRT). Monte Carlo experiments are performed to evaluate the detection performance of the proposed detector. Experimental results illustrate that the proposed detector outperforms its competitors in scenarios with limited secondary data.

Adaptive target detection against spatially correlated compound‐Gaussian clutter with multivariate inverse Gaussian texture

To improve the detection performance in the presence of target-steering vector mismatches, a novel robust adaptive matched filter (AMF) detector for compound Gaussian clutter is proposed. First, the multivariate inverse Gaussian distribution is first introduced to compound Gaussian clutter model. Second, the texture value is estimated with maximum a posteriori (MAP) estimator in spatial domain, which has a closed form and is not affected by the target's steering vector mismatch. A robust AMF detector is derived based on this estimation. Simulation results demonstrate that the proposed detector can achieve better performance in the presence of target-steering vector mismatches.

Near-optimum coherent CFAR detection of radar targets in compound-Gaussian clutter with inverse Gaussian texture

This paper studies the design of efficient detector for radar targets in compound-Gaussian clutter with inverse Gaussian texture (CG-IG clutter). Due to inclusion of the modified Bessel function, the optimum coherent detector in CG-IG clutter cannot be easily implemented in radar systems. Through deriving the mathematical relationship between the shape parameters of the K and CG-IG distributions, the efficient α matched filter (α-MF) detector in K-distributed clutter, is improved to realize the near-optimum detection with real-time and fast implementation in CG-IG clutter. The control parameter α in CG-IG clutter is calculated by a new formula. The constant false alarm rate (CFAR) property of α-MF and adaptive α-MF (α-AMF) is proved in CG-IG clutter. Numerical results indicate that the α-AMF CFAR detector has almost the same detection performance as the adaptive optimum detector and outperforms the adaptive MF (AMF) and adaptive normalized MF (ANMF) in CG-IG clutter.

Estimators of compound Gaussian clutter with log-normal texture

ABSTRACTEstimators of clutter models parameters based upon higher order moments estimator (HOME) produce usually poor results in particular for low sample sizes. In an attempt to remedy this situation, closed forms of [zlog(z)] and fractional order moments estimator (FOME) are derived in this work and yield a good estimation accuracy of parameters of the compound Gaussian clutter with log-normal texture (CG-LNT). Using simulated and real data, estimation comparisons show that best values of mean square error (MSE) and bias are achieved using the proposed procedures.

Adaptive two-step Bayesian MIMO detectors in compound-Gaussian clutter

The problem of adaptive target detection in compound-Gaussian clutter with unknown covariance matrix for multiple-input multiple-output (MIMO) radar is addressed in this paper. A set of secondary data for each receiver is assumed to be available, and the primary data and the secondary data own the same covariance matrix structure but different power levels (textures). Firstly, a Bayesian approach is proposed, where the structure is modeled as a random matrix with an appropriate distribution. Then, two ways are adopted to model the texture: an unknown deterministic quantity or a random variable ruled by certain distribution. In this framework, three adaptive generalized likelihood ratio tests (GLRTs) are developed using the two-step design procedure. Finally, the capabilities of the proposed detectors and their superiority with respect to some existing techniques are evaluated via numerical simulations.