Adaptive Generalized Likelihood Ratio Test Research Articles

Long-time adaptive coherent detection of small targets in sea clutter by fast inversion algorithm of block tridiagonal speckle covariance matrices

Low-velocity small target detection in maritime surveillance radars is always a challenging task. Low signal-to-clutter ratio requires long-time coherent integration to obtain enough gain of target returns. However, long-time coherent integration encounters insufficient secondary data due to the spatial inhomogeneity of sea clutter. In this paper, considering the decorrelation time of speckle component of sea clutter short up to a dozen of milliseconds, the spherical invariant random vector (SIRV) model with block tridiagonal speckle covariance matrix and the inverse Gamma distributed texture is proposed to model sea clutter sequences in several tenths of a second. In this model, a long-time adaptive generalized likelihood ratio test with linear threshold detector (GLRT-LTD) is constructed. Owing to the block tridiagonal structure of speckle covariance matrices, the adaptive detection requires much less reference cells for speckle covariance matrix estimation and much lower computational cost for its inversion. The proposed detector is verified by an X-band high-resolution island-based radar data with an unmanned aerial vehicle (UAV) as test target. The experimental result shows that it obtains competitive detection performance in comparison with the state-of-the-art detectors.

Adaptive Jamming Attack Detection Under Noise Uncertainty in mmWave Massive MIMO Systems

This paper addresses jamming attack detection issue in a millimeter Wave (mmWave) massive MIMO system under noise uncertainty. Specifically, we apply the generalized likelihood ratio test (GLRT) to develop a one-step GLRT scheme for detecting jamming attack under the homogeneous and partially homogeneous noise environments, and exploit training data to estimate the unknown noise statistical information and replace it with resulting estimation in deriving GLRT procedure to obtain adaptive GLRT detection scheme. We also design a two-step GLRT scheme, where we first assume the unknown noise statistical information is known, and derive GLRT based on test data, and then replace it by the sample covariance matrix based on training data only to achieve a fully adaptive jamming attack detector. With the help of statistical signal subspace analysis and composite hypothesis testing theories, we further examine the statistical distributions of the jamming attack detection schemes and present the closed-form expressions of false alarm and detection probabilities for the proposed schemes under different noise environments. Finally, we implement extensive simulations to validate the theoretical results and evaluate the detection efficiency under various parameters.

Bi-Phase Compound-Gaussian Mixture Model of Sea Clutter and Scene-Segmentation-Based Target Detection

In high-resolution maritime surveillance radars, sea clutter exhibits highly spatial heterogeneity due to modulation of long waves with wavelengths longer than the width of one range cell. Compound-Gaussian model (CGM) fails to characterize the heterogeneous high-resolution sea clutter in both amplitude distribution and Doppler spectrum. In this article, a bi-phase compound-Gaussian mixture model (BP-CGMM) is proposed to characterize the heterogeneous sea clutter. In the BP-CGMM, spatial resolution cells are grouped into two disjoint sets, and the sea clutter in each set is represented by one CGM with inverse Gamma-distributed texture. The spectral heterogeneity indicates that sea clutter vectors at spatially adjacent resolution cells in one set share the same speckle covariance matrix, while that at two adjacent spatial cells separated in the two sets often have different speckle covariance matrices. The BP-CGMM is validated by a mass of measured high-resolution sea clutter data. Moreover, under the BP-CGMM, a detection method based on batch test is given to detect sea-surface small targets, which is composed of scene segmentation, by the aid of Bayesian threshold and morphological filtering, and adaptive generalized likelihood ratio test linear-threshold detector (GLRT-LTD) separately in each set. The detection method is verified by measured data with small targets under test. The experimental results show that it attains better detection performance than the adaptive GLRT-LTD under the CGM of sea clutter.

Adaptive Generalized Likelihood Ratio Test for Change Detection in SAR Images

Generalized likelihood ratio test (GLRT) was widely used in the change detection of synthetic aperture radar (SAR) images and employed as a competitive method in many studies. GLRT is applied to the neighborhood areas corresponding to the same geographical area in multitemporal SAR images. Generally, the difference image is acquired by applying the GLRT on a moving window empirically determined by the user, which is clearly unreasonable because the different pixels in an image may have different optimal windows. To overcome this drawback, we propose an adaptive GLRT (AGLRT) method for change detection in SAR images. AGLRT adaptively selects the homogeneity window for each pixel and can decrease the uncertainty of change detection due to manual selection of the moving window. Experiments are conducted on two data sets to confirm the performance of the proposed method. Experimental results show that AGLRT can both suppress the influence of noise and preserve edge details and improve the accuracy of change detection.

Shape-parameter-dependent coherent radar target detection in K-distributed clutter

In this paper, shape-parameter-dependent matched filter (MF) detectors are proposed for moving target detection in K-distributed clutter, which are specified by a single parameter α [0, 1], the α-MF detectors for short. The α-MF detectors include the MF and normalized matched filter (NMF) detectors as special examples with α = 0 and 1. The parameter α can be chosen to match clutter characteristics. An empirical formula is given that the optimal parameter α equals the number of integrated pulses divided by it plus the shape parameter of K-distributed clutter. It is proved that the α-MF detectors are constant false alarm rate (CFAR) with respect to the scale parameter, clutter covariance matrix, and Doppler steering vector. The properties of adaptive α-MF (α-AMF) detectors are discussed. For K-distributed clutter, the optimal α-MF detectors are superior to the MF and NMF detectors and are comparable with the optimal K-distributed detectors (OKDs) of high computational cost. Finally, real high-resolution sea clutter data are available to verify the proposed detectors. The optimal α-AMF detectors under K-distributed clutter model are competitive in performance with the adaptive generalized likelihood ratio test linear threshold detectors (GLRT-LTDs) that are optimal for the compound-Gaussian clutter with the inverse Gamma texture.

Distributed target detection with polarimetric MIMO radar in compound-Gaussian clutter

This paper mainly deals with distributed targets detection with a polarimetric MIMO radar against compound-Gaussian clutter dominated scenario with unknown covariance matrix. First, the general polarimetric detecting problem of the distributed targets is developed to the MIMO radar, and then, the fully adaptive Generalized Likelihood Ratio Test (GLRT) is devised according to the well known two-step design procedure. Three covariance matrix estimation strategies using the secondary data are introduced to make derived receiver fully adaptive. A thorough performance assessment is given, and via several numerical examples, the results highlight that the spatial and polarization diversities can be exploited to improve the detection performance of the distributed targets in compound-Gaussian background, and it outperforms the phased-array counterpart, the adaptive loss is completely acceptable in practical applications. Meanwhile, the fixed-point estimation strategy is more suitable to implement the adaptive detection algorithm.

ROBUST ADAPTIVE DETECTION AGAINST SIRV CLUTTER IN THE PRESENCE OF STEERING VECTOR MISMATCHES

This paper mainly deals with the problem of detecting a target against spherically invariant random vector (SIRV) clutter in the presence of steering vector mismatches. Assuming that the mismatch of the steering vector satisfles the conic constraint, the generalized likelihood ratio test (GLRT) is devised, and the geometry description is proposed for the derived solution. Additionally, the fully adaptive GLRT is derived by replacing the exact covariance with flxed point estimate (FPE). Finally, several numerical results are provided and discussed.

A GLRT-based spread-spectrum receiver for joint channel estimation and interference suppression

The author considers the problem of demodulating a direct-sequence (DS) spread-spectrum signal in the presence of narrowband interference and multipath. A receiver is considered that is based on the generalized likelihood-ratio test (GLRT), in which the interferer is modeled as an Nth-order circular Gaussian autoregressive (AR) process and the multipath channel is represented by a tapped-delay line. The maximum-likelihood joint estimator for the channel coefficients and interferer AR parameters is then derived. Analytical expressions for bit-error rate are presented for GLRT receiver, under the assumption of perfect estimates of the channel and interferer parameters. The performance of the GLRT receiver is compared to that of a DS receiver using a transversal equalizer. It is shown that the GLRT receiver consistently outperforms the equalizer-based receiver by 2-3 dB. The performance of an adaptive GLRT receiver is evaluated where the recursive least-squares algorithm is used to jointly estimate the interferer and channel parameters.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>