Detection In compound-Gaussian Clutter Research Articles

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.

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.

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.

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 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.

Adaptive Radar Detectors Based on the Observed FIM

Modified versions of Rao, Wald, and Durbin tests are considered exploiting an estimator of the Fisher Information Matrix (FIM) in place of the exact one. They are asymptotically equivalent (under some technical conditions) to the standard counterparts and rely on the use of the Observed FIM (OFIM), which is proportional to the negative Hessian of the log-likelihood. The developed framework is applied to the problem of adaptive radar detection of a point-like target in homogeneous or partially-homogeneous interference. Remarkably, for both the scenarios, it is shown that Rao, Wald, and Durbin tests with OFIM are statistically equivalent to the Generalized Likelihood Ratio Test (GLRT) for the specific detection problem (namely Kelly's detector for the homogeneous environment and the Adaptive Coherence Estimator (ACE) [S. Kraut and L. L. Scharf, “The CFAR adaptive subspace detector is a scale-invariant GLRT,” IEEE Trans. Signal Process., vol. 47, no. 9, pp. 2538-2541, Sep. 1999.], also known as Adaptive Normalized Matched Filter (ANMF) [E. Conte, M. Lops, and G. Ricci, “Asymptotically optimum radar detection in compound-gaussian clutter,” IEEE Trans. Aerosp. Electron. Syst., vol. 31, no. 2, pp. 617-625, Apr. 1995], for the partially-homogeneous scenario). This provides a new interpretation of the mentioned GLRTs laying the foundations for a better understanding of their theoretical validity.

Distributed Airborne MIMO Radar Detection in Compound-Gaussian Clutter without Training Data

This paper deals with the detection problem of a moving target for the distributed airborne multi-input multi-output radar embedded in the compound-Gaussian and non-homogeneous clutters, without assuming that training data are available. A novel detector combing the Bayesian approach and the generalized likelihood ratio test is proposed, where we model the covariance of clutter as an inverse Wishart distribution with an unknown average clutter covariance matrix (ACCM). More precisely, we regard the unknown ACCM as a structured matrix with the Hadamard product form involving two independent parts, composing of the covariance matrix taper (CMT) and the Doppler spectrum component. Further, the proposed detector exploits a nonlinear processing in an iteration fashion to reconstruct sparse signals with the aim of estimating the unknown spectrum of clutters. As to the CMT component, we resort to generalized CMT model to improve estimation accuracy. Numerical simulations are provided to assess the capability of the proposed detector in different complicated scenarios.

Polarimetric Detection in Compound Gaussian Clutter With Kronecker Structured Covariance Matrix

In this paper, we consider polarimetric adaptive detection in compound Gaussian clutter whose covariance matrix (CM) has a Kronecker structure. We derive the Cramer–Rao bound of a Kronecker structured CM estimate and analyze the constant false alarm rate property of the adaptive subspace matched filter detector that uses Kronecker structured estimates. We provide a general expression for the average signal-to-clutter ratio loss (SCRL) as a function of the mean square error of the covariance estimate. The aforementioned expression is helpful in determining how many samples are required in order to achieve a desired average SCRL level in practical scenarios. Based on that expression, we show that the required sample size can be effectively reduced by exploiting the Kronecker structure of the clutter CM. We also derive the asymptotic detection performance of the adaptive subspace matched filter. The analysis of SCRL and detection performance can be extended to more general scenarios, especially when the maximum-likelihood estimate of the structured CM involves solving fixed point equations. Numerical simulations validate the merits of the proposed methods.

Coherent radar detection in Compound-Gaussian clutter: Clairvoyant detectors

This paper provides a historical and tutorial overview of coherent radar target detection in compound-Gaussian clutter and offers some new perspectives and avenues of research in this challenging area. It begins with a brief introduction that motivates the need to develop statistical models of non-Gaussian clutter and then reviews some of the physical ideas that led to modeling multivariate radar clutter statistics by the compound-Gaussian model. With this starting point, the paper then reviews a series of ideas that have been developed to describe clairvoyant detectors in such clutter. The term “clairvoyant” refers to the assumption that the properties of the clutter are assumed to be known. In a practical scenario, this assumption does not hold and adaptive techniques are needed to estimate clutter properties and implement the detector. Such techniques are guided however by the appropriate clairvoyant detector structures and hence it is proper to start by studying these detectors. As part of this review, the paper offers new ways of looking at this problem that suggest new research topics. This review is limited to the problem of clairvoyant detection in which the relevant properties of the clutter are assumed to be known. Adaptive detection in compound- Gaussian clutter will be the topic of a subsequent tutorial that the authors are preparing.

An enhanced p-norm energy detector for coherent multilook detection in X-band maritime surveillance radar

This paper introduces a new variation of the p-norm detector, which is designed for application to coherent multilook detection in compound Gaussian clutter with inverse Gamma texture. By applying what is termed a compensator, enhanced detection performance can be achieved independently of the number of looks used. This is particularly useful in the case of a fast scan rate radar where the number of looks may be quite small. Conventional coherent detectors tend to experience saturation in such scenarios, and so this new detection process complements recent advances in this area. Further validation is provided by applying this new decision rule to synthetic target detection in real X-band radar clutter.

Track-before-detect strategies for range distributed target detection in compound-Gaussian clutter

This paper considers range distributed target detection in compound-Gaussian clutter, which is often arisen in high resolution radars. Two dynamic programming based track-before-detect (DP-TBD) strategies, i.e., noncoherent integration based DP-TBD (NCI-DP-TBD) and the generalized likelihood ratio test based DP-TBD (GLRT-DP-TBD) strategies, are proposed to address the detection problem. The detection performances of the proposed strategies are examined via numerical simulations.

Adaptive range-spread maneuvering target detection in compound-Gaussian clutter

Adaptive detection of range spread maneuvering target embedded in compound-Gaussian clutter is an important challenge for radar engineers. For the long integration, maneuvering target suffers from inevitable range walks across cells as well as unpredictable phase change at individual range cells. Therefore, the traditional adaptive normalized matched filter detectors are ineffective in long integration duration. In this paper, combining adaptive normalized matched filter with sliding high-order cross-correlation integration, a new adaptive range-spread target detector is proposed. Firstly, the long integration duration is segmented into short disjoint subintervals. In each subinterval, it is assumed that no range walking across cells happens and target's complex returns accord with the traditional rank-one parameter models. In each subinterval, the coherent integration output vector is obtained by the adaptive normalized matched filter along the slow-time dimension in different range cells. If a target is present, the coherent integration outputs of each subinterval share similar waveforms except for unknown range walks. Otherwise, when a target is absent, the coherent integration output vectors estimated from individual subintervals are uncorrelated positive random vectors without similarity. Further, the sliding high-order cross-correlation integration of these coherent integration output vectors is calculated for target detection. The new detector combines the short-time coherent integration and long-time similarity integration. The experimental results using raw radar target data and simulated clutter data show that the new detector achieves better detection performance for range-spread maneuvering targets than the existing detectors.

Mimo Radar Detection in Compound Gaussian Clutter Using Orthogonal Discrete Frequency Coding Space Time Waveform

This paper proposes orthogonal Discrete Frequency Coding Space Time Waveforms (DFCSTW) for Multiple Input and Multiple Output (MIMO) radar detection in compound Gaussian clutter. The proposed orthogonal waveforms are designed considering the position and angle of the transmitting antenna when viewed from origin. These orthogonally optimized show good resolution in spikier clutter with Generalized Likelihood Ratio Test (GLRT) detector. The simulation results show that this waveform provides better detection performance in spikier Clutter.

Robust Detection in Compound Gaussian Clutter Based on Bayesian Framework

复合高斯杂波中的纹理分量决定了杂波的非高斯性，而纹理分量的不确定性会影响常规检测器的性能。基于Bayes框架，该文采用先验分布描述杂波纹理分量的不确定性，分析先验模型选择对检测器检测性能与稳健性的影响。先验信息模型包括无信息先验分布和有信息先验分布。无信息先验分布包括Jeffery先验模型和广义无信息先验模型两种，所得到的检测器结构就是归一化匹配滤波器(NMF)。有信息先验模型采用共轭先验分布，得到的是一种知识辅助的归一化匹配滤波器(KA-NMF)，该检测器结构与判决门限都是先验分布参数的函数，该文分析了KA-NMF检测性能对先验分布参数的敏感性。进一步采用无信息先验模型描述先验分布参数，可以获得分层Bayes归一化匹配滤波器(HB-NMF)。计算机仿真与实测海杂波数据分析结果表明，HB-NMF的性能与分布参数无关，稳健性优于KA-NMF，而检测性能优于NMF。

RANGE-SPREAD TARGET DETECTION IN COMPOUND GAUSSIAN CLUTTER WITH RECIPROCAL OF THE SQUARE ROOT OF GAMMA TEXTURE

In this paper, the range-spread target detection in compound Gaussian clutter with reciprocal of the square root of gamma (RSRG) texture is investigated. The RSRG distribution has been proved to be a good model for texture component of extremely heterogeneous radar clutter. Taking this compound Gaussian model as a spherically invariant random process (SIRP), the Neyman-Pearson optimal detector for the range-spread target detection with known target amplitude is derived flrstly. By replacing the ideal target amplitude with its maximum likelihood estimate (MLE), the generalized likelihood ratio test (GLRT) is then obtained. The statistical property of the texture component is taken into account in both of these two detectors, which makes the detectors computationally complicated. A suboptimal generalized likelihood ratio test based on order statistics (OS-GLRT) is flnally proposed by substituting the texture component with its MLE. The OS-GLRT makes use of some largest observations from the range cells occupied by the most likely target scatters. The performance assessment conducted by Monte Carlo simulation validates the efiectiveness of the proposed detectors.

Wideband Radar Spread Targets Detection in Compound Gaussian Clutter

The aim of this study is to analyze the influence of neglecting compound Gaussian clutter texture on wideband radar targets detection. The texture of compound-Gaussian clutter is researched, the Probability Density Function (PDF) expression of G0 distributed clutter is proposed. The optimal detection statistics treats the texture of G0 distributed clutter as a certain function is derived. As contrast, another detector neglects the clutter texture is derived. The numerical results are presented by means of Monte Carlo simulation strategy. Assume that cells of signal components are available. Those secondary data are supposed to possess either the same covariance matrix or the same structure of the covariance matrix of the cells under test. The simulation results highlight that the performance loss of the two detectors in different roughness parameter, the result shows the loss less than 2dB due to the texture is neglected and adaptively estimating the covariance matrix.

A Persymmetric GLRT for Adaptive Detection in Compound-Gaussian Clutter With Random Texture

We focus on the problem of detecting a signal in compound-Gaussian clutter, where the texture is a random variable with Gamma or inverse Gamma distribution. The persymmetric structure of the covariance matrix is exploited and a persymmetric generalized likelihood ratio test (Per-GLRT) using a three-step procedure is proposed. In addition, we prove that the Per-GLRT ensures constant false alarm rate (CFAR) property with respect to the covariance matrix. Finally, the detector is assessed by Monte Carlo simulations. Performance comparison of the Per-GLRT with the traditional GLRT shows that the former improves the detection performance in training-limited scenarios.

Coherent CFAR detection in compound Gaussian clutter with inverse gamma texture

Recent publications have explored coherent radar detection in a compound Gaussian clutter environment with inverse gamma texture, since the latter clutter model has been validated for X-band high-resolution maritime surveillance radar clutter returns. This paper explores the development of coherent constant false alarm rate (CFAR) detectors for this scenario. In the first instance, a detector is constructed with explicit knowledge of the clutter parameters. It is then shown that the probability of false alarm/threshold relationship does not vary with the clutter power. To achieve a CFAR detector, clutter parameter approximations are then introduced, and the cost associated with this is then analysed.

Low-Grazing Angle Detection in Compound-Gaussian Clutter with Hybrid MIMO Radar

This paper focuses on the target detection in low-grazing angle using a hybrid multiple-input multiple-output (MIMO) radar systems in compound-Gaussian clutter, where the multipath effects are very abundant. The performance of detection can be improved via utilizing the multipath echoes. First, the reflection coefficient considering the curved earth effect is derived. Then, the general signal model for MIMO radar is introduced in low-grazing angle; also, the generalized likelihood test (GLRT) and generalized likelihood ratio test-linear quadratic (GLRT-LQ) are derived with known covariance matrix. Via the numerical examples, it is shown that the derived GLRT-LQ detector outperforms the GLRT detector in low-grazing angle, and both performances can be enhanced markedly when the multipath effects are considered.

Adaptive MIMO Radar Design and Detection in Compound-Gaussian Clutter

Multiple-input multiple-output (MIMO) radars with widely separated transmitters and receivers are useful to discriminate a target from clutter using the spatial diversity of the scatterers in the illuminated scene. We consider the detection of targets in compound-Gaussian clutter, describing heavy-tailed clutter distributions fitting high-resolution and/or low-grazing-angle radars in the presence of sea or foliage clutter. First we introduce a data model using the inverse gamma distribution to represent the clutter texture. Then we apply the parameter-expanded expectation-maximization (PX-EM) algorithm to estimate the clutter texture and speckle as well as the target parameters. We develop a statistical decision test using these estimates and demonstrate its statistical characteristics. Based on the statistical characteristics of this test, we propose an algorithm to adaptively distribute the transmitted energy among the transmitters and maximize the detection performance. We demonstrate the advantages of the MIMO setup and adaptive energy allocation in target detection in the presence of compound-Gaussian clutter using Monte Carlo (MC) simulations.