Target 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.

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.

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.

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.