Real Clutter Data Research Articles

A Novel Method for Sea-Land Clutter Separation Using Regularized Randomized and Kernel Ridge Neural Networks.

Classification of clutter, especially in the context of shore based radars, plays a crucial role in several applications. However, the task of distinguishing and classifying the sea clutter from land clutter has been historically performed using clutter models and/or coastal maps. In this paper, we propose two machine learning, particularly neural network, based approaches for sea-land clutter separation, namely the regularized randomized neural network (RRNN) and the kernel ridge regression neural network (KRR). We use a number of features, such as energy variation, discrete signal amplitude change frequency, autocorrelation performance, and other statistical characteristics of the respective clutter distributions, to improve the performance of the classification. Our evaluation based on a unique mixed dataset, which is comprised of partially synthetic clutter data for land and real clutter data from sea, offers improved classification accuracy. More specifically, the RRNN and KRR methods offer 98.50% and 98.75% accuracy, outperforming the conventional support vector machine and extreme learning based solutions.

Non‐linear Kalman filters comparison for generalised autoregressive conditional heteroscedastic clutter parameter estimation

In this work, the authors analyse the estimation of the generalised autoregressive conditional heteroscedastic (GARCH) process conditional variance based on three non-linear filtering approaches: extended Kalman filter (EKF), unscented Kalman filter and cubature Kalman filter. The authors present a state model for a GARCH process and derive an EKF including second-order non-linear terms for simultaneous estimation of state and parameters. Using synthetic data, the authors evaluate the consistency and the correlation of the innovations for the three filters, by means of numerical simulations. The authors also study the performance of smoothed versions of the non-linear Kalman filters using real clutter data in comparison with a conventional quasi-maximum likelihood estimation method for the GARCH process coefficients. The authors show that with all methods the process coefficients estimates are of the same order and the resulting conditional variances are commensurable. However, the non-linear Kalman filters greatly reduce the computational load. These kind of filters could be used for the radar detector based on a GARCH clutter model that uses an adaptive threshold that demands the conditional variance at each decision instant.

Efficient modelling of SAR texture with a gamma‐inverse gamma distribution for MAP‐based speckle suppression

The precise statistical modelling of synthetic aperture radar (SAR) texture is crucial while formulating maximum a posteriori (MAP) filter for speckle suppression. In this study, the authors introduce an SAR texture model considering the mixture of gamma and inverse gamma Γ I Γ distribution as an approximation to a generalised inverse Gaussian (GIG) distribution, which suitably portrays areas with a varying degree of heterogeneity. An estimator is proposed for the Γ I Γ model parameters using the expectation–maximisation (EM) algorithm. Cramer-Rao bounds are also derived for the Γ I Γ model parameters to evaluate the effectiveness of the EM estimator. Furthermore, the Γ I Γ model is experimentally established as an approximation to the GIG distribution through Monte Carlo simulation. Suitability and applicability of the Γ I Γ model is then validated through 1-look real clutter and multilook synthetic clutter data over textured areas. Utilising the above Γ I Γ model as prior density, the authors proposed an MAP filter for speckle suppression in SAR clutter data from areas of a diverse kind. Finally, the effectiveness of the Γ I Γ -MAP filter is assessed using different statistical measures and is found superior over the MMSE-based Lee, Kuan filters and MAP-based Γ -MAP, β -MAP, G 0 -MAP, CE-MAP filters in terms of speckle suppression and mean preservation.

Comprehensive measurement and modeling in foliage environments using UHF UWB ground-based radar

The precise knowledge of the statistical properties of clutter plays a key role in radar target detection in foliage environments. Owing to a wealth of multiple scattering, the selection of an appropriate model for diverse foliage environments remains a conundrum. Thus the statistical modeling of the foliage clutter in different environments is focused on in this paper. In particular, the primary contribution is to characterize and parameterize the amplitude of the foliage clutter in three typical foliage environments of different densities, including line-of-sight (LOS) forest environment, non line-of-sight (NLOS) forest-penetration environment, and NLOS bush-penetration environment. Based on the real clutter data acquired with an ultra-high frequency (UHF) ultra-wideband (UWB) ground-based radar, the experimental results show that the specific foliage type and configuration has a significant impact on the distribution of foliage clutter. Furthermore, by means of Kullback–Leibler distance (KLD), log-logistic, and distributions with estimated parameters are demonstrated to be the best choice to model the clutter in the LOS forest environment, NLOS forest-penetration environment and NLOS bush-penetration environment, respectively. The proposed models are great advantages on understanding and prediction of the clutter in foliage environments.

A divergence mean-based geometric detector with a pre-processing procedure

This paper proposes a divergence mean-based geometric detector to deal with the problem of target detection in a clutter with the limited sample data. In particular, a covariance matrix is used to model the correlation of sample data in each cell in one coherent processing interval. This modeling method can avoid the poor Doppler resolution as well as the energy spread of the Doppler filter banks result from the fast Fourier transform. Moreover, a pre-processing procedure, conceived from the philosophy of the bilateral filtering in image denoising, is proposed and combined within the geometric detection framework. As the pre-processing procedure acts as the clutter suppression, the performance of geometric detector is improved. Numerical experiments and real clutter data are given to validate the effectiveness of our proposed method.

Geometric target detection based on total Bregman divergence

This paper develops a geometric detection approach based upon the total Bregman divergence on the Riemannian manifold of Hermitian Positive-Definite (HPD) matrices to realize target detection in a clutter. First of all, the radar received clutter data in each range cell in one coherent processing interval is modeled and mapped into an HPD matrix space, which can be described as a complex Riemannian manifold. Each point of this manifold is an HPD matrix. Then, a class of total Bregman divergences are presented to measure the closeness between HPD matrices. Based on these divergences, the medians for a finite collection of HPD matrices are derived. Furthermore, the three divergences, namely the total square loss, the total log-determinant divergence, and the total von Neumann divergence are deduced, and their corresponding geometric detection methods are designed. The principle of detection is that if a location has enough dissimilarity from the median estimated by its neighboring locations, targets are supposed to appear at this location. Numerical experiments and real clutter data are given to demonstrate the effectiveness of the proposed geometric detection methods.

Binary integration nonparametric detection for range-spread targets in distributed terahertz radar network under unknown clutter

In this study, to detect person-borne concealed threats in range profiles under the circumstance of unknown clutter, we propose a binary integration nonparametric detection method based on the generalized sign (GS) detector for range-spread targets in a distributed terahertz radar network (DTRN). In the detection, the length of range-spread targets and the number of dominant scatterers on range-spread targets are considered and adaptively estimated. Furthermore, the GS detection method is applied to maintain a constant false alarm rate (CFAR) under the circumstance of unknown clutter. The detection performance of the proposed method for single terahertz radar and DTRN are both examined with the data synthesized by real range-spread targets data and real clutter data. Experimental results show that the proposed method is effective, and for a given false alarm probability, the DTRN exhibits better detection performance than the single terahertz radar.

Low RCS Target Tracking in Estimated Rapidly Varying Sea Clutter Using a Kronecker Product Approximation Algorithm

We propose a method for estimating the space-time covariance matrix of rapidly-varying sea clutter following a dynamic state space matrix model. The covariance matrix dimension can become computationally infeasible as it increases with the number of range bins and dwell pulses required for coherent processing. In order to reduce the computational complexity, we apply the Kronecker product (KP) approximation and particle filtering to estimate the space-time covariance matrix, and we demonstrate the proposed method's validity using real clutter data. We also demonstrate that the method ensures that the covariance matrix estimate is always positive definite. The covariance matrix estimation is integrated with a track-before-detect filter for tracking a low radar cross-section (RCS) target in strong sea clutter.

Projection Matrix Optimization for Sparse Signals in Structured Noise

We consider the problem of estimating a signal which has been corrupted with structured noise. When the signal of interest accepts a sparse representation, only a small number of measurements are required to retain all the information. The measurements are mapped to a lower dimensional space through a projection matrix. We propose a method to optimize the design of this matrix where the objective is not only to reduce the amount of data to be processed but also to reject the undesired signal components. As a result, we reduce the computation time and the error on the estimation of the unknown parameters of the sparse model, with respect to the uncompressed data. The proposed method has tunable parameters that can affect its performance. Optimal tuning would require a comprehensive study of parameter variations and options. To avoid this learning burden, we also introduce a variant of the algorithm that is free from tuning, without significant loss of performance. Using synthetic data, we analyze the performance of the proposed algorithms and their robustness against errors in the model parameters. Additionally, we illustrate the performance of the method through a radar application using real clutter data with a still target and with a synthetic moving target.

Optimised binary integration with order statistic CFAR in Pareto distributed clutter

This paper examines the application of binary integration to X-band maritime surveillance radar, with a view to enhancing detection performance. The clutter is assumed to follow a Pareto distribution, since this model has been validated for high resolution X-band maritime clutter returns. The binary integration process is based upon an order statistic detection scheme, which has the constant false alarm rate property with respect to the Pareto shape parameter. An optimisation procedure is outlined, which results in ideal choices for the binary integration factor and the order statistic index. Performance of the resultant detection process is analysed, with homogeneous and heterogeneous simulated clutter, whose parameters are matched to those obtained from real clutter data sets. A direct application to real data is also included.

复合高斯杂波中知识辅助检测器的先验信息感知方法

The radar targets detection performance can be improved by using some prior information, but the mismatches between the prior information and current detection environments may result to the degradation of the knowledge aided detector performance. In this issue, a cognitive method of the clutter texture distribution parameters is proposed for the knowledge detector in compound Gaussian clutter under the Bayesian framework. Firstly, the general cognitive method of prior information for the knowledge aided detection is explained. Then for the knowledge aided detector using prior information of clutter texture component, the quantized relationship between the mismatches of prior information and detector's performance is given. Furthermore, using the test statistics and measurement of false alarm probability obtained by the knowledge aided detector under current clutter environment, the constrained relationship of texture component distribution parameters is constructed. The cognitive value of these clutter texture distribution parameters can be calculated by the intersection analysis of multiple constrained relationships. The computer simulation is used to analyze the feasibility of cognition method, and the real clutter data sets are used to validate the cognitive method. The detection performance of knowledge detector using cognitive value can be improved furthermore.

ADAPTIVE DETECTION IN COMPOUND-GAUSSIAN CLUTTER WITH INVERSE GAUSSIAN TEXTURE

This paper mainly deals with the problem of target detection in the presence of Compound-Gaussian (CG) clutter with the Inverse Gaussian (IG) texture and the unknown Power Spectral Density (PSD). The traditional CG distributions, in particular the K distribution and the complex multivariate t distribution, are widely used for modeling the real clutter data from the High-Resolution (HR) radars. Recently, the novel CG distribution with the IG texture is described as the IG-CG distribution and validated to provide the better flt with the recorded data of the HR clutter than the mentioned two competitors. Within the IG-CG framework, the detector is flrstly proposed here in terms of the two-step Generalized Likelihood Ratio Test (GLRT) criterion, and the empirical estimation method is resorted to estimate the unknown PSD in order to adapt the realistic scenario. The proposed detector is tested on the real-life HR clutter data, in comparison with the Adaptive Normalized Matched Filter (ANMF) processor, and the detection results illustrate that it outperforms the ANMF.

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.

Polarization diversity detection in compound-gaussian clutter

We present the problem of polarization diversity detection in compound-Gaussian clutter with unknown covariance matrix. To this end we assume that a set of secondary data, free of signal components and with the same covariance structure of the cell under test, is available. Due to the lack of a uniformly most powerful (UMP) detector we resort to a design procedure based upon the Rao and the Wald tests. Specifically we first derive the Rao and the Wald tests assuming that the covariance matrix is known, and then we substitute into the derived decision rules a suitable estimate of the clutter covariance. Interestingly, the newly proposed detectors share the constant false alarm rate (CFAR) property with respect to the texture statistical characterization. Moreover simulation results have shown that the Wald test based detector ensures a performance level higher than the Rao test. We have also conducted a further performance analysis, in the presence of real clutter data and in comparison with the previously proposed generalized likelihood ratio test (GLRT) based receivers, which highlights that, in general, the Wald test receiver outperforms its counterparts. Finally, since the newly proposed decision rules as well as the previously designed GLRTs do not ensure the CFAR property with respect to the clutter covariance matrix, we have developed a sensitivity analysis on the probability of false alarm (P/sub fa/), based on simulated clutter with covariance matrix estimated from real radar data. The results have shown that (P/sub fa/) is only slightly affected by variations in the clutter correlation properties and hence the CFARness is substantially achieved.

Multiframe detector/tracker: optimal performance

We develop the optimal Bayes multiframe detector/tracker for rigid extended targets that move randomly in clutter. The performance of this optimal algorithm provides a bound on the performance of any other suboptimal detector/tracker. We determine by Monte Carlo simulations the optimal performance under a variety of scenarios including spatially correlated Gaussian clutter and non-Gaussian (K and Weibull) clutter. We show that, for similar tracking performance, the optimal Bayes tracker can achieve peak signal-to-noise ratio gains possibly larger than 10 dB over the commonly used combination of a spatial matched filter (spatial correlator) and a linearized Kalman-Bucy tracker. Simulations using real clutter data with a simulated target suggest similar performance gains when the clutter model parameters are unknown and estimated from the measurements.