non-Gaussian Sea Clutter Research Articles

Adaptive Persymmetric Subspace Detection in Non-Gaussian Sea Clutter With Structured Interference

Adaptive Persymmetric Subspace Detection in Non-Gaussian Sea Clutter With Structured Interference

Persymmetric Detection of Radar Targets in Nonhomogeneous and Non-Gaussian Sea Clutter

This article addresses the detection problem of radar targets embedded in nonhomogeneous and non-Gaussian sea clutter. Nonhomogeneity leads to insufficiency of secondary data for estimating the clutter speckle covariance matrix, and non-Gaussianity causes sea clutter to become spiky. In this article, the persymmetry of the clutter covariance matrix is adopted to alleviate the requirement of secondary data, and the prior distribution of clutter texture is exploited to tackle the clutter non-Gaussianity. Based on such clutter knowledge, three adaptive detectors are proposed according to the principles of the generalized likelihood ratio test, the Wald test, and the Rao test. It is proven that three detectors ensure constant false alarm rate (CFAR) properties with respect to both the clutter speckle covariance matrix and the clutter power mean. Simulation experiments show that three detectors outperform their competitors.

Wald- and Rao-Based Detection for Maritime Radar Targets in Sea Clutter With Lognormal Texture

Non-Gaussian sea clutter causes conventional detectors designed in Gaussian clutter to suffer detection performance degradation, and some nuisance parameters cause the uniformly most powerful test to be unavailable. To improve the detection performance of maritime radar targets, we investigate the design of adaptive detectors in correlated non-Gaussian sea clutter via using suboptimal tests. The non-Gaussian sea clutter is modelled as a product of lognormal-distributed texture and complex Gaussian speckle. Two adaptive radar target detectors are developed by using the suboptimal two-step Wald and Rao tests. Specifically, a non-adaptive detector is derived by the Wald or Rao test when the clutter texture and speckle covariance matrix are assumed to be known in the first step; then the clutter parameters known in the first step are estimated, and the true parameters of the detector obtained in the first step are replaced with the estimated values. Theoretical proof and experimental verification indicate that the two proposed detectors have the constant false alarm property with regard to the clutter speckle covariance matrix and the clutter average power. Numerical results on simulated and measured radar data show that the proposed Rao-based detector outperforms its competitors, and has the stronger robustness to the signal mismatch compared to the proposed Wald-based detector.

Persymmetric Adaptive Target Detection With Dual-Polarization in Compound Gaussian Sea Clutter With Inverse Gamma Texture

This paper investigates the dual-polarimetric target detection problem in non-Gaussian sea clutter. The compound Gaussian model with inverse Gamma texture is adopted to describe the real sea clutter of which characteristics are time-varying and heterogeneous. Three novel polarimetric detectors are proposed based on the two-step maximum <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori</i> (MAP) generalized likelihood ratio test (GLRT), MAP Rao, and MAP Wald criteria. Specifically, we assume the polarimetric clutter covariance matrix (PCCM) and the inverse Gamma textures are known in the first step, and the test statistics of the proposed polarimetric detectors are derived. In the second step, we take advantage of the proposed polarimetric persymmetric property and MAP criterion to estimate the PCCM and inverse Gamma textures, respectively. The fully adaptive persymmetric polarimetric detectors are obtained by substituting the estimates into the test statistics of the proposed detectors. Then the proposed detectors are proved to have the constant false alarm rate (CFAR) properties with respect to the real PCCM. The performance assessments are evaluated by contrasting the proposed detectors with their counterparts on the simulated and measured sea clutter data. The numerical results verify the performance of the proposed persymmetric polarimetric detectors.

GLRT-Based Polarimetric Detection in Compound-Gaussian Sea Clutter With Inverse-Gaussian Texture

This letter presents the derivation of the generalized likelihood ratio test (GLRT)-based polarimetric detector in the non-Gaussian clutter. We model the non-Gaussian sea clutter as compound-Gaussian distribution with inverse-Gaussian texture (IG-CG), which has better goodness-of-fit for the high-resolution sea clutter. Based on the two-step GLRT criterion, we develop the test statistic of the proposed detector by assuming the texture component is known in the first step. In the second step, with the texture of the secondary data estimated as the power of the clutter, we insert the maximum <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori</i> estimate (MAPE) of the texture of the primary data into the test statistic to achieve the fully adaptive detection. Furthermore, the model-based polarimetric detector, which exploits the independence between the co-polarized and the cross-polarized component, is derived. Finally, we conduct experiments with simulated clutter and real sea clutter to evaluate the performance of the proposed detector. The simulation results verify that the proposed detectors have better detection performance and the MAPE under different hypotheses leads to different levels of robustness to the mismatched signal.

Bayesian Detection for Radar Targets in Compound-Gaussian Sea Clutter

We consider the detection problem of maritime radar targets in the training-sample-starved and non-Gaussian sea clutter environment. The performance of conventional detectors for radar targets is seriously degraded due to both the starvation of training samples for estimating the clutter covariance matrix and the non-Gaussianity of sea clutter. In this letter, we adopt the inverse Gaussian distribution and the inverse complex Wishart distribution to model the texture and speckle covariance matrix of sea clutter, respectively. Then an adaptive Bayesian detector is developed based on the two-step generalized likelihood ratio test and the maximum posterior estimates of clutter parameters. Finally, the experimental results on simulated and measured data demonstrate the performance superiority of the proposed detector over its competitors, especially when the training samples are starved.

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.

CFAR Strategy Formulation and Evaluation Based on Fox’s H-function in Positive Alpha-Stable Sea Clutter

The problem of target detection in impulsive non-Gaussian sea clutter has attracted a lot of attention in recent years. The positive alpha-stable (PαS) distribution has been validated as a suitable model for the impulsive non-Gaussian sea clutter. Since the probability density function (PDF) of the PαS variable cannot be expressed as a closed-form expression, the research into constant false alarm rate (CFAR) detectors in PαS distributed sea clutter is limited. This paper formulates and evaluates some CFAR detectors, such as Greatest Of-CFAR (GO-CFAR), Smallest Of-CFAR (SO-CFAR), Order Statistic-CFAR (OS-CFAR) and censored mean level (CML) detectors, in PαS distributed sea clutter. Firstly, the Fox’s H-function is adopted to express the PDF of the PαS variable, and the cumulative density function based on Fox’s H-function is derived in this paper. Then, by use of the properties of the H-function and PαS distribution, exact expressions of the probabilities of false alarm and detection for CFAR detectors in the PαS background are derived. Some CFAR properties of these detectors in the PαS background are also explored. Numerical results based on derived expressions are given and verified by Monte Carlo simulation. Some analyses of detection performance from a practical perspective are also given.

Model for Non-Gaussian Sea Clutter Amplitudes Using Generalized Inverse Gaussian Texture

In this letter, we focus on the statistical modeling of sea clutter amplitudes. Due to its non-Gaussian nature, the existing statistical models are sometimes difficult to represent well the heavy-tailed portion of amplitude distribution. To address this problem, we propose a compound Gaussian (CG) model with a generalized inverse Gaussian (GIG) texture to describe sea clutter amplitudes. In this regard, the probability density function and the cumulative distribution function of the clutter amplitudes for the proposed model are derived. Moreover, we provide an approach to estimate the unknown parameters of the proposed CG-GIG distribution. The experimental results indicate that the CG-GIG distribution is more suitable to describe the amplitudes of non-Gaussian sea clutter than its competitors.

Анализ статистических характеристик помех от морской поверхности

As is known, when radars with long impulses observe a sea surface area in a range much larger than the seawave length, the clutter in the form of reflections from the sea surface has the Gaussian distribution, and their envelope has the Rayleigh distribution. As the impulse width decreases (i.e., as the radar resolution range is increased along with significant influence of small grazing angles), the clutter distribution differs from the Gaussian one, and the envelope distribution law differs from the Rayleigh distribution in having a longer tail. The clutter becomes impulsive in nature. Thus, the radar receiver designed proceeding from the standard assumption that the sea clutter obeys the Gaussian distribution becomes less effective, because the false alarm probability is increased due to an incorrectly chosen object detection threshold that is not designed for the impulse nature of clutter. Hence, a need arises to choose the most adequate model of disturbing sea clutter. The article presents a review and analysis of the main models of non-Gaussian sea clutter (lognormal, Weibull and K-distribution) that are used to describe disturbing sea clutter. Much attention is paid to the model based on the K-distribution. The experimental records of sea clutter obtained using the IPIX coherent-pulse radar were compared with those obtained using the main sea clutter models. The study results have shown that the lognormal model tends to overestimate the dynamic range of the clutter actual distribution, which means that the distribution has a longer tail. The Rayleigh model tends to underestimate the dynamic range; that is, the tail is significantly shorter. The clutter model based on the Weibull distribution offers much broader possibilities for statistically modeling sea clutter than those offered by the lognormal model or the Rayleigh model. Depending on the parameters, the Weibull distribution can transform into the Rayleigh or lognormal distribution, thus reflecting the distribution of real clutter in a more accurate manner. In comparison with the other considered models, the sea clutter model based on the K-distribution describes the sea clutter envelope structure most accurately, even in the zone of distribution tails. Thus, the clutter model based on the K-distribution is the most promising one for describing the sea clutter envelope in a radar with short direct impulses.

Mitigation Techniques for Non-Gaussian Sea Clutter

This paper deals with the problem of coherent radar detection of targets embedded in clutter modeled as a compound-Gaussian process. We first provide a survey on clutter mitigation techniques with a particular emphasis on adaptive detection schemes ensuring the constant false-alarm rate (CFAR) property with respect to all of the clutter parameters. Thus, we propose a novel decision rule based on a recursive covariance estimator, which exploits the persymmetry property of the clutter covariance matrix. Remarkably, the devised receiver is fully CFAR in that its threshold can be set independently of the clutter distribution as well as of its covariance, even if the environment is highly heterogeneous; namely, the disturbance distributional parameters vary from cell to cell. At the analysis stage, we compare the performance of the novel detector with some classical radar receivers such as that of Kelly and the adaptive matched filter both in the presence of simulated as well as on real radar data, which statistical analysis has shown to be compatible with the compound-Gaussian model. The results show that the new receiving structure generally provides higher detection performance than the others and, for a fluctuating target, it uniformly outperforms the counterparts. We also provide a discussion on the CFAR behavior of the analyzed receivers as well as on their computational complexity.

Wavelet detection scheme for small targets in sea clutter

A radar detection scheme based on wavelet determination of scatterer lifetime within the Doppler spectra of non-Gaussian sea clutter is reported. The method is shown to be complementary to simple intensity thresholding when applied to a low-observable time varying target.

Texture modelling, estimation and validation using measured sea clutter data

The authors consider modelling and estimating the texture in high resolution non-Gaussian sea clutter. Cyclostationarity of sea clutter is investigated and validated by processing measured high-resolution data. The clutter is modelled as a compound Gaussian process and the texture as the superposition of real cosines with unknown frequencies, amplitudes and phases. A method is proposed for estimating the model parameters and retrieving the texture component from the intensity data in the presence of multiplicative noise with unknown power spectral density. The method exploits the clutter cyclostationarity and is based on a RELAXation optimisation approach. The ability of the proposed method to retrieve texture information is investigated by processing simulated and measured sea clutter data.

General class of non-Gaussian coherent clutter models

The author proposes a scheme to treat radar clutter as a non-Gaussian process. The approach is based on the concept of a fluctuating number of Gaussian scatterers. These scatterers can be of different types and may be grouped into classes on the basis of common statistical properties such as their covariance matrix and their number distribution functions (DFs). Using this idea, general expressions for the in-phase (I) and quadrature (Q) time-sampled joint multivariate probability density functions (PDF) for clutter and clutter plus Ricean target are first obtained and analysed. Starting with these general expressions, corresponding univariate PDFs for the amplitude squared or radar cross-section (RCS) are also obtained. Next, a simple example of one class of fluctuating scatterers plus a Gaussian background is considered in greater detail. In this model a negative binomial number DF is used to describe the fluctuations in the number of scatterers. The resulting PDF of the clutter RCS is a four-parameter function which under certain conditions is reduced to the K-PDF. The parameters are then expressed in terms of the first four moments of the PDF, and using some highly non-Gaussian sea clutter data the model is shown to fit the data better than other popular models. The paper concludes with a discussion of the merits of the model and of possible extensions.

Further Results on the Detection of Targets in Non-Gaussian Sea Clutter

A recent work calculated the probabilities of detection and false alarm for the mean and median detectors for the log-normal and contaminated-normal descriptions of sea clutter. This correspondence extends the previous work by first calculating the performance of the trimmed-mean detector and then calculating the detection performance of all three detectors when the target exhibits Rayleigh fluctuations. Finally, a simple example is used to show that the optimal beamwidth of a radar need not be the smallest obtainable beamwidth.

Detection of Targets in Non-Gaussian Sea Clutter

Measurements of sea clutter using high-resolution radar show that clutter cross section is not Rayleigh distributed. Both the log-normal and contaminated-normal distributions are proposed and yield a fairly good description of some experimental data. Threshold values and detection curves are given for the mean and median detectors for both distributions. It is shown that the median is robust_that is, the threshold values and detection probabilities do not depend on the detailed shape of the clutter distribution but only on its median value.