Targets In compound-Gaussian Clutter Research Articles

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

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.

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.

Adaptive subspace detection of range-spread target in compound Gaussian clutter with inverse Gaussian texture

In this paper, we investigate the adaptive subspace detection of range-spread target embedded in compound Gaussian clutter. The inverse Gaussian distribution is considered to describe the texture of the clutter in order to match the non-Gaussian characteristic of sea clutter. Moreover, the range-spread target's energy is assumed to be spread not only in range dimension but also in Doppler dimension, which is more suitable for the practical situation. Thus, the range-spread target is modeled as a subspace signal which describes the range-spread and Doppler-spread. In this paper, an adaptive detector for a subspace range-spread target is derived by using two-step generalized likelihood ratio test (GLRT). Finally, the detection performance of the proposed detector is verified by experiments based on measured data and simulation data. The experimental results show that the detection performance of the proposed detector is better than that of the existing detectors.

Detection of Range Migrating Targets in Compound-Gaussian Clutter

This paper deals with the problem of coherent radar detection of fast-moving targets in a high-range resolution mode. In particular, we are focusing on the spiky clutter modeled as a compound Gaussian process with rapidly varying power along range. Additionally, a fast-moving target of interest has a few range cells migration within the coherent processing interval. Two coherent CFAR detectors are proposed taking into account target migration and highly inhomogeneous clutter. Both detectors involve solution of a transcendental equation, carried out numerically in a few iterations. The performance evaluation is addressed by numerical simulations and it shows a significant improvement in detection of fast-moving targets in inhomogeneous heavy tailed radar clutter.

Adaptive detection of range-spread target in compound-Gaussian clutter without secondary data

In this paper, we address the problem of detecting a range-spread target embedded in a non-Gaussian clutter with unknown covariance matrix and without using secondary data. We propose a new autoregressive method based on the generalized likelihood ratio test (GLRT) that requires only the cells under test. This method is used to derive two new detectors, corresponding to two different scenarios: a) when all range cells contain the target and share the same covariance matrix (homogeneous clutter), b) when different covariance matrices for different range cells are assumed (heterogeneous clutter). The proposed method is shown to outperform the state of the art on various scenarios in terms of false alarm probability and detection probability, especially in critical scenario as small data records or low number of secondary data. Finally, it exhibits the desired constant false alarm rate (CFAR) property.

Adaptive detection of range-spread targets in compound Gaussian clutter with the square root of inverse Gaussian texture

The compound Gaussian clutter with the square root of inverse Gaussian texture component has been successfully used for modeling the heavy-tailed non-Gaussian clutter measured by high-resolution radars. In high-resolution radars, the targets may extend along multiple consecutive range cells, which are called range-spread targets. In this paper, we consider the range-spread target detection problem in the compound Gaussian clutter with the square root of inverse Gaussian texture. Three adaptive detectors are proposed based on Bayesian one-step generalized likelihood ratio test, maximum a posteriori generalized likelihood ratio test and Bayesian two-step generalized likelihood ratio test, respectively. Finally, the detection performances of the proposed detectors are evaluated by the Monte Carlo simulation. The simulation results show that the proposed detectors have better detection performance of range-spread target than the conventional generalized likelihood ratio test detector.

Adaptive detection of distributed targets in compound-Gaussian clutter with inverse gamma texture

The problem of coherent detection for distributed target in compound-Gaussian clutter with inverse gamma texture is studied and three detectors. One-step generalized likelihood ratio test (GLRT), maximum a-posteriori GLRT and two-step GLRT, are proposed respectively in a Bayesian architecture. Resultantly, these detectors have similar detection structures with their test statistics modulated by the shape and scale parameters of the texture. Alternatively, they can be reformulated into another form with their test statistics associated with the scale parameter and detection thresholds related with the shape parameter. And this detection structure can be seen as a matched filter form with a shape-parameter-dependent threshold like the detectors for point target. Subsequently, the proposed detectors are compared with two-step GLRT based on compound-Gaussian clutter without considering texture model, their detection performances are evaluated, and their robustness are analyzed via Monte Carlo simulations. Results enlighten us that: (1) the three Bayesian detectors bear pretty much the same detection performances; (2) the detection performances fluctuate more intensely when the shape parameter or the scale parameter is smaller; (3) the shape parameter has more influences on the detection performances than the scale parameter, as it is an indication of the clutter impulsiveness.

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.

AR-model-based adaptive detection of range-spread targets in compound Gaussian clutter

In this paper, we consider the problem of adaptive detection for range-spread targets with known Doppler and unknown complex amplitude in compound Gaussian clutter. The speckle component of the clutter is modeled as an autoregressive (AR) process. By using the generalized likelihood ratio test (GLRT) approach, we will first estimate the AR parameters and the unknown complex amplitude, and then propose an adaptive AR-based GLR detector. The performance assessments are presented too. The computer simulations show that the proposed detector, without a priori information of the covariance matrix, has the same asymptotical performances as the two-step GLR-based detector with known covariance matrix.

Performance analysis of GLRT-based adaptive detector for distributed targets in compound-Gaussian clutter

The problem of adaptive detection for spatially distributed targets in compound-Gaussian clutter is studied. We first derive the optimum NP detector and suboptimum two-step GLRT detector. For the two-step detection strategy, we also introduce three covariance matrix estimation strategies and evaluate their CFAR properties and complexity issues. Next, the numerical results are presented by means of Monte Carlo simulation strategy. In particular, the simulation results highlight that the performance loss due to adaptively estimating the texture is negligible, and that the loss due to adaptively estimating covariance matrix largely depends on the estimation algorithm, the number of the secondary data vectors and the number of the scatterers.

Adaptive polarimetry design for a target in compound-Gaussian clutter

We develop optimal adaptive design of radar waveform polarizations for a target in compound-Gaussian clutter. We present maximum likelihood estimates of the target's scattering matrix and clutter parameters using a parameter-expanded expectation-maximization (PX-EM) algorithm. We compute the Cramér–Rao bound (CRB) on the target's scattering matrix. To design the polarization, we propose an algorithm that minimizes a CRB function. We propose also suboptimal versions of this algorithm and illustrate the performance as well as compare our algorithm with numerical examples.

Adaptive subspace detection of range-distributed target in compound-Gaussian clutter

This paper deals with high-resolution radar (HRR) adaptive detection of range-distributed target embedded in compound-Gaussian clutter which is modeled as a spherically invariant random process (SIRP). Using multiple dominant scattering (MDS) model of range-distributed target, we justify that range-distributed target can be modeled as a subspace random signal. The unknown deterministic parameters are replaced by their ML estimates and then the nonadaptive detector is proposed. A closed-form expression for the probability of false alarm of the nonadaptive detector is derived and it ensures CFAR property with respect to the unknown statistics of the clutter texture component. Moreover, an adaptive detector is obtained relying on a two-step GLRT-based design procedure. Performances of these proposed detectors are assessed through Monte Carlo simulations and are shown to have better detection performance compared with existing similar detector.

Polarization diversity detection of distributed targets in compound-gaussian clutter

We deal with the problem of polarimetric detection of compound-Gaussian clutter with unknown distributed targets in covariance matrix. Since no uniformly most powerful (UMP) detector exists for the problem at hand, we devise and assess two classes of suboptimum receivers. The former contains two detectors, designed according to the Wald and the generalized likelihood ratio tests, which resort to secondary data, free of signal components and with the same covariance structure of the cells under test, for estimating the clutter spectral properties. The latter contains a detector which achieves adaptivity without exploiting the training set All the decision rules ensure the constant false alarm rate (CFAR) property with respect to the texture statistics but they are not theoretically CFAR with respect to the disturbance covariance matrix. Finally, we present simulation results, based also on real clutter data, showing that the Wald test receiver achieves in general a better detection performance and a stronger robustness than its counterparts.