Gaussian Clutter Research Articles

Persymmetric direction detector for range‐spread target in subspace interference plus homogeneous and partially homogeneous clutter

AbstractThe problem of detecting the direction of range‐spread targets in the presence of subspace interference, along with homogeneous and partially homogeneous Gaussian clutter backgrounds characterised by an unknown persymmetric covariance matrix, is being investigated. It is assumed that the interference subspace is linearly independent of the signal subspace, and the coordinates of both subspaces are unknown. Leveraging the property of persymmetry, the two‐step Generalised Likelihood Ratio Test and the two‐step Wald test are proposed to formulate three detectors in the presence of subspace interference, homogeneous, and partially homogeneous Gaussian clutter. Theoretical derivations and simulations demonstrate that three proposed detectors maintain a constant false alarm rate with respect to the unknown clutter covariance matrix. In comparison to existing detectors in similar backgrounds, particularly in scenarios with limited training data, these three detectors exhibit superior detection performance.

Adaptive detection of distributed targets in heterogeneous Gaussian clutter without secondary data

This paper addresses the problem of detecting distributed targets in heterogeneous Gaussian clutter without assuming the presence of secondary data. Specifically, the clutter is modeled as a spherically invariant random process with unknown texture components and covariance matrix structure (CMS). In contrast to existing approaches that are based on the estimate-and-plug techniques, we introduce an approximation of the generalized likelihood ratio test that leverages an alternating estimation procedure to obtain at least a local likelihood maximum. We also prove that the proposed method achieves the constant false alarm rate with respect to clutter parameters when appropriately initialized. Finally, a comprehensive performance analysis is carried out by Monte Carlo simulation and in comparison with the non-scatterer density dependent generalized likelihood ratio test (NSDD-GLRT) in the cases of known and unknown CMS. The results show that the proposed solution is more robust and effective than NSDD-GLRT when the CMS is unknown while exhibiting only a modest performance degradation with respect to the benchmark when the CMS is known.

Adaptive Detection of Point Targets in Compound- Gaussian Clutter With Inverse Gamma Texture

Adaptive Detection of Point Targets in Compound- Gaussian Clutter With Inverse Gamma Texture

Knowledge-Aided Bayesian Detection of Distributed Target for FDA-MIMO Radar in Gaussian Clutter

Knowledge-Aided Bayesian Detection of Distributed Target for FDA-MIMO Radar in Gaussian Clutter

Çoklu Yansıma Ortamlarında Geniş Menzilli Hedeflerin Uyarlanabilir Radar Tespiti

This paper discusses the adaptive detection of extended radar targets buried in Gaussian clutter, assuming a diffuse multipath environment. The target return signal from each range cell is modeled as the sum of a deterministic data vector, which includes an unknown scaling factor representing the direct path component, and a randomly distributed data vector in a Gaussian distribution with unknown covariance matrix representing multipath echoes. During the design phase, it is assumed that the primary data covariance matrix falls within the vicinity of a sample covariance matrix that is devised from the secondary data set. The paper proposes a constraint Generalized Likelihood Ratio Test (GLRT) for the adaptive detection problem of extended radar targets in diffuse multipath environments, and conducts a performance analysis comparing the developed algorithm with well-known adaptive detectors in the literature. The results and performance analysis demonstrate that the proposed approach enhances the detection performance of extended radar targets in environments with diffuse multipath. Overall, this article provides valuable insights for improving the adaptive detection of extended targets in challenging environments, with potential applications in radar and sensing technologies.

Robust moving target detection for FDA-MIMO radar with steering vector mismatches

This paper studies a robust moving target detection problem for frequency diverse array multiple-input multiple-output (FDA-MIMO) radar with a target embedded in Gaussian clutter with an unknown covariance matrix. We focus on the case of signal mismatch where the errors exist in the transmit-receive vector and Doppler steering vector. To solve the above detection problem, we adopt the subspace approach, where the true and nominal transmit-receive and Doppler steering vectors are assumed to lie in four-dimensional and two-dimensional subspaces, respectively, for the special framework for FDA-MIMO radar. At the detector design stage, we propose three robust adaptive detectors based on the one-step generalized likelihood ratio test (GLRT), two-step GLRT, and Wald test, respectively, with the training data free. The proposed detectors own the constant false alarm rate (CFAR) property against the clutter covariance matrix. The numerical examples validate the effectiveness of the proposed 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.

Persymmetric subspace Gradient-based detectors for distributed targets in homogeneous and partially homogeneous environments

In this paper, we consider the problem of adaptive detection for distributed targets in zero-mean Gaussian clutter with unknown persymmetric covariance matrix. The target signal is assumed to locate in a known subspace with unknown coordinates. Two persymmetric subspace detectors are designed by utilizing the Gradient criterion in homogeneous and partially homogeneous environments, respectively. Additionally, the proposed detectors are theoretically confirmed to be constant false alarm rate to the clutter covariance matrix and the power level. Moreover, numerical results show that the proposed detectors perform better than the existing competitors, especially in training-limited scenarios.

Persymmetric adaptive detection of range-spread targets in subspace interference plus Gaussian clutter

Persymmetric adaptive detection of range-spread targets in subspace interference plus Gaussian clutter

Subspace gradient tests for range‐spread target detection in structured interference plus Gaussian clutter

AbstractThis study considers the problem of detecting range‐spread targets embedded in subspace interference plus Gaussian clutter with an unknown covariance matrix. The target and interference signals are modeled in terms of deterministic signals belonging to two known subspaces, respectively. Based on the Gradient test criterion, two adaptive detectors are devised for rejecting subspace interference in homogeneous and partially homogeneous environments, respectively. Both of the proposed detectors theoretically exhibit a desirable property of a constant false alarm rate with respect to the clutter covariance matrix as well as the power level. Furthermore, the numerical results show that, compared with their existing counterparts, the proposed detectors exhibit better detection performance and satisfactory suppression performance for the interference.

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.

Adaptive multiple targets detection for FDA-MIMO radar with Gaussian clutter

This paper presents an adaptive multiple targets detection framework for frequency diverse array multiple-input multiple-output radar embedded in Gaussian noise with an unknown covariance matrix. To this end, we define the one-range-cell multiple targets model as a summation expression and then design four detectors, i.e., the generalized likelihood ratio test, adaptive matched filter, Rao, and Wald tests for the above newly built problem. Closed-form expressions for the probability of false alarm and the probability of detection are provided to assess the aforementioned detectors. The numerical examples show that the proposed architectures can ensure better detection performance than the considered competitors. Finally, notice that each proposed detector has a specific behavior in terms of robustness to low volumes of data. Thus, the choice of a specific solution is dictated by the operating requirements of the radar system.

Persymmetric subspace GLRT-based detector for range-spread targets

In this paper, adaptive signal detection is addressed for range-spread targets in unknown zero-mean Gaussian clutter with persymmetric covariance matrix. The range-spread target can be expressed as the product of a known subspace and its unknown arbitrary coordinates. Under the above assumptions, a persymmetric subspace detector is devised by utilizing the generalized likelihood ratio test (GLRT). Moreover, the proposed persymmetric subspace GLRT-based detector is theoretically proved to be constant false alarm rate to the unknown clutter covariance matrix. Finally, the numerical results demonstrate the effectiveness of the proposed detector, compared with the existing unstructured competitors and persymmetric ones, especially in the limited training data scenarios.

Adaptive Distributed Target Detection for FDA-MIMO Radar in Gaussian Clutter Without Training Data

Since frequency diverse array multiple-input multiple-output (FDA-MIMO) radar possesses additional target range information for potential performance improvement, this article studies adaptive distributed targets detection for FDA-MIMO radar, where the targets are embedded in Gaussian clutter with unknown covariance matrix. The proposed FDA-MIMO radar detection model considers also the distributed targets occupying several secondary range cells, which is different from the classic detection models in multiple-input multiple-output (MIMO) and/or phase array (PA) radars that discuss only point-like targets. By exploiting the FDA-MIMO radar framework for distributed target detection, we propose the detector through a two-step generalized likelihood ratio test criteria without the need of training data and/or a priori covariance matrix. Moreover, closed-form expressions for the probability of false alarm and detection probability are derived, respectively. The proposed detector adheres to the property of a constant false alarm rate because its probability of false alarm is not restricted by the covariance matrix. The proposed method together with all theoretical analysis are verified by numerical results.

Persymmetric subspace detector for distributed target in partially homogeneous environment

This paper deals with the problem of distributed target detection in partially homogeneous Gaussian clutter whose covariance matrix is unknown but persymmetric. It is assumed that primary data and training data share the same clutter covariance matrix structure but different power levels. The target signal is supposed to lie in a multi-rank subspace with unknown coordinates. A persymmetric subspace detector is designed based on the generalised likelihood ratio test criteria. It is theoretically demonstrated that the proposed detector possesses constant false alarm rate property with respect to the unknown clutter covariance matrix as well as the power level. Experimental results illustrate the performance advantage of the proposed detector over the existing competitors, especially in training-limited scenarios.

Bayesian Detection in Gaussian Clutter for FDA-MIMO Radar

This paper investigates the Bayesian detection problem for a moving target that is embedded in a homogeneous Gaussian clutter with an unknown but stochastic covariance matrix for frequency diverse array multiple-input multiple-output (FDA-MIMO) radar. First, we propose a Bayesian detector based on structured generalized likelihood ratio test (SGLRT), namely BSGLRT, criteria that requires no training data. Then, we present a detector based on the Bayesian unstructured generalized likelihood ratio test (BUGLRT) to reduce the three dimensions (range-angle-Doppler) search into one-dimension Doppler searches for low-complexity implementation in practical applications. Moreover, the robustness of the BSGLRT and BUGLRT detectors is also analyzed. Numerical results reveal that the proposed Bayesian detectors and estimators, i.e. BSGLRT and BUGLRT, outperform their non-Bayesian counterparts in Gaussian clutter with a small number of snapshots and/or low signal-to-clutter rate (SCR) for FDA-MIMO radar.

Detection of Multi-Pixel Low Contrast Object on a Real Sea Surface

Video images captured at long range often show low-contrast floating objects of interest on a sea surface. A comparative experimental study of the statistical characteristics of reflections from floating objects and from the agitated sea surface showed differences in the correlation and spectral characteristics of these reflections. The functioning of the recently proposed modified matched subspace detector (MMSD) is based on the separation of the observed data spectrum on two subspaces: relatively low and relatively high frequencies. In the literature, the MMSD performance has been evaluated in general and using only a sea model (i.e., additive Gaussian background clutter). This paper extends the performance evaluating methodology for low contrast object detection using only a real sea dataset. The methodology assumes an object of low contrast if the mean and variance of the object and the surrounding background are the same. The paper assumes that the energy spectrum of the object and the sea are different. The paper investigates a scenario in which an artificially created model of a floating object with specified statistical parameters is placed on the surface of a real sea image. The paper compares the efficiency of the classical matched subspace detector (MSD) and MMSD for detecting low-contrast objects on the sea surface. The article analyzes the dependence of the detection probability at a fixed false alarm probability on the difference between the statistical means and variances of a floating object and the surrounding sea.

Adaptive Multiple Targets Detection for Fda-Mimo Radar with Gaussian Clutter

Adaptive Multiple Targets Detection for Fda-Mimo Radar with Gaussian Clutter

Adaptive Detection With Bayesian Framework for FDA-MIMO Radar

In this letter, we present an adaptive Bayesian detection framework for frequency diverse array multiple-input multiple-output (FDA-MIMO) radar. The targets are detected in Gaussian clutter with unknown but stochastic covariance matrix. We designed two detectors in the Bayesian framework, namely, Bayesian Rao (BRao) detector and Bayesian Wald (BWald) detector without training data. Numerical results reveal that the proposed detectors outperform conventional non-Bayesian counterparts. It is necessary to note that the BWald detector requires higher computational complexity than the BRao detector.

A General Framework for Slow and Weak Range-Spread Ground Moving Target Indication Using Airborne Multichannel High-Resolution Radar

Airborne multichannel high-resolution radar (HRR)-ground moving target indication (GMTI) is of great significance to wide-area surveillance, traffic monitoring and target recognition. The Aerospace Information Research Institute, Chinese Academy of Sciences, produced an advanced airborne digital array radar with high resolution and conducted an experiment with slow and weak cooperative moving targets in 2021. In this paper, an overall processing framework for target detection, parameter estimation and target tracking using this system is introduced. For HRR detection, the echo energy of targets is spread into multiple range units, so-called range-spread targets; thus, using detectors designed for point-like targets will severely degrade the detection performance, especially for slow and weak targets. To address the adaptive detection of such range-spread targets embedded in Gaussian clutter with an unknown covariance matrix, a novel two-step generalized space-time adaptive processing (GSTAP) algorithm is proposed, which offers an enhanced clutter suppression capability compared with natural competitors. Moreover, a tracking method is implemented in the range-Doppler domain to avoid track loss caused by azimuth relocation error and reject discrete false alarms. Both simulation and experimental results are presented to demonstrate the effectiveness of the proposed method and provide a paradigm for further research.