Generalized Likelihood Ratio Test Detector Research Articles

KB-GLRT: exploiting knowledge of the clutter ridge in airborne radar

Knowledge-based radar detection for space-time adaptive processing applications is addressed. At the design stage, the characteristic structure of the clutter ridge was taken account of and two decision rules were devised according to the generalised likelihood ratio test (GLRT) and the two-step GLRT criteria. The authors first deal with the case of a clutter ridge with integer slope and then discuss the more general framework of a non-integer slope. For the latter case only approximate GLRT detectors are provided due to the analytical difficulties connected with the exact solution of the problem. Analysis shows that the new knowledge-based systems can outperform some previously proposed adaptive schemes and can achieve a performance level very close to the optimum detector, which assumes the perfect knowledge of the clutter covariance matrix.

Vector subspace detection in compound-Gaussian clutter. Part I: survey and new results

Deals with the problem of detecting subspace random signals against correlated non-Gaussian clutter exploiting different degrees of knowledge on target and clutter statistical characteristics. The clutter process is modeled by the compound-Gaussian distribution. In the first part of the paper, the optimum Neyman-Pearson (NP) detector, the generalized likelihood ratio test (GLRT), and a constant false-alarm rate (CFAR) detector are sequentially derived both for the Gaussian and the compound-Gaussian scenarios. Different interpretations of the various detectors are provided to highlight the relationships and the differences among them. In particular, we show how the GLRT detector may be recast into an estimator-correlator form and into another form, namely a generalized whitening-matched filter (GWMF), which is the GLRT detector against Gaussian disturbance, compared with a data-dependent threshold. In the second part of this paper, the proposed detectors are tested against both simulated data and measured high resolution sea clutter data to investigate the dependence of their performance on the various clutter and signal parameters.

Strengths and limitations of the Fourier method for detecting accelerating targets by pulse Doppler radar

The Fourier transform method has been generally used in pulse Doppler radar for detecting targets that are moving with acceleration, despite the phenomenon known as Doppler smearing which limits the performance of the method. Examples of accelerating targets are manoeuvring aircraft and missiles. The authors quantify the effects of Doppler smearing. In using a pulse Doppler radar to detect a nonaccelerating target in additive white Gaussian noise and to estimate its radial velocity, the Fourier method provides an output signal-to-noise ratio (SNR) that increases linearly with the number of pulses. When the target is accelerating, the Fourier method may still be used to detect the target and estimate its median velocity, provided the acceleration is small enough in the sense described. For a given acceleration, when the number of pulses is increased, the output SNR of the Fourier method varies as a concave function, increasing to a maximum and then decreasing, before the method fails catastrophically. Thus the number of pulses and the acceleration have to be matched to achieve optimum performance. Empirical formulas for the dependence of the optimum SNR and the optimum number of pulses on the acceleration are given. The results are shown to be relevant to the design of generalised likelihood ratio test detectors that apply a search over a grid.

Noncoherent multiuser detection for CDMA systems with nonlinear modulation: a non-Bayesian approach

This paper considers the problem of multiuser detection for a system in which each user employs nonlinear modulation, with an emphasis on noncoherent detection techniques which do not require knowledge of the users' channel parameters at the receiver. Our goals are to gain fundamental insight into the capabilities of multiuser detection in such a setting, and to provide practical algorithms that perform better than conventional matched-filter reception. We begin by providing fundamental performance benchmarks by considering coherent maximum-likelihood (ML) detection, which requires knowledge of the users' channel parameters, as well as noncoherent detection, formulated in a non-Bayesian generalized likelihood ratio test (GLRT) framework. The asymptotic performance of each detector, as the noise level vanishes, is characterized, yielding simple geometric criteria for near-far resistance. In general, both the ML and GLRT detectors have complexity which is exponential in the number of users. We, therefore, propose the more practical sequential decision projection (SDP) detector which has complexity which is quadratic in the number of users. It is shown that the SDP detector has nonzero asymptotic efficiency if the users' powers are suitably disparate.

Space–time processing for the detection of airborne targets in IR image sequences

The problem of detecting airborne targets in a sequence of images recorded by a long range infra-red sensor is investigated. The generalised likelihood ratio test detector is derived. The detector structure and its actual implementation are discussed. Approximated expressions of the false alarm and detection probabilities are obtained and validated by means of simulations. Example results obtained on a set of experimental data are reported.

Modified GLRT signal detection algorithm

The problem of signal detection in unknown Gaussian interference utilizing an adaptive array is considered here. Two modified generalized likelihood ratio test (GLRT) detectors are suggested that improve on the performance of Kelly's GLRT when the interference has low-rank property and the sample support for estimating the array covariance matrix is limited. Under these adverse conditions, the sample covariance matrix might be ill conditioned. This problem may be overcome either by diagonal loading of the covariance matrix or rank reduction of the data. The two modified GLRT detectors are referred to as loaded GLRT (L-GLRT) and reduced-rank GLRT (RR-GLRT), respectively. Expressions are developed for the probabilities of false alarm and detection. The advantage of the proposed detectors is illustrated by numerical examples.

A computationally efficient two-step implementation of the GLRT

In this paper, the performance of a new two-step adaptive detection algorithm is analyzed. The two-step GLRT consists of an initial adaptive matched filter (AMF) test followed by a generalized likelihood ratio test (GLRT). Analytical expressions are provided for the probability of false alarm (P/sub FA/) and the probability of detection (P/sub D/) in unknown complex Gaussian interference. The analysis shows that the two-step GLRT significantly reduces the computational load over the GLRT while maintaining detection and sidelobe rejection performance commensurate with the GLRT. The two-step GLRT detection algorithm is also compared with another two-step detection algorithm: the adaptive sidelobe blanker (ASB). Both the two-step GLRT and the ASB are characterized in terms of the mainbeam detection performance and the rejection of sidelobe targets. We demonstrate that for a given P/sub FA/, the two-step GLRT has a broad range of threshold pairs (one threshold for the AMF test and one for the GLRT) that provide performance identical to the GLRT. This is in contrast with the ASB, where the threshold pairs that maximize the P/sub D/ are a function of the target's signal-to-interference-plus-noise ratio (SINR). Hence, for a fixed pair of thresholds, the two-step GLRT can provide slightly better mainbeam detection performance than the ASB in the transition region from low to high detection probabilities.

A GLRT detector for bursty targets in multiplicative noise

A robust generalized likelihood ratio test (GLRT) detector is derived for targets in multiplicative noise with gamma-Weibull statistics. The target is assumed to have a spatial distribution (burstiness) modeled by the incomplete binomial distribution. The GLRT detector is found to be similar in performance to the optimized power law detector.

Nonlinear STAP processing

The paper deals with the cancellation of clutter echoes in modern airborne early warning (AEW) radar systems equipped with digital beam-forming. The performances of reduced degrees of freedom (DoF) GLRT (generalised likelihood ratio test) detectors and the previously conceived nonlinear detector are compared. The nonlinear detector is demonstrated to be a compromise solution between computational requirements and detection performance. Moreover, it shows much higher robustness to nonhomogeneous clutter. A MATLAB-based software tool is used to show the results that can be achieved using live data recorded by the experimental radar AAFTE (Adaptive Array Flight Test Equipment) of the US Naval Research Laboratory.

Optimal processing and performance evaluation of passive acoustic systems

The acoustic spectrogram is a fundamental input for many underwater acoustic signal detectors. This presentation describes an effort to place theoretical bounds on the performance of these detectors and to provide insight into the structure of practical processors. The presentation begins with a description of the Neyman–Pearson optimal detector, the likelihood ratio test, and its use for signal detection in colored Gaussian noise scenarios. The presentation continues by developing successively more complex detection scenarios that necessitate the use of background estimation and, consequently, the generalized likelihood ratio test (GLRT). Parameter estimation from several common noise spectral estimation (NSE) techniques is included in the GLRT detection scenarios. In addition, the presentation develops guidelines for matching NSE parameter settings to signal characteristics. Then, the relationships among several relevant signal-to-noise ratios (SNRs) is developed and their correlation with detection performance is considered. Finally, the presentation considers departures from the Gaussian distribution assumption for background and signal processes and the resulting consequences. a)Ensign, USN

The use of spectral information in optimal detection of a source in the presence of a directional interference

The detection of a passive sonar target in the presence of ambient noise and a plane wave interference is discussed. Intuitively, such a detector consists of a spatial filter which nulls the interference, followed by a temporal filter. In this paper we study the role of the a priori knowledge of the spectrum of the interference and/or signal in improving detector performance. We develop three different generalized likelihood ratio test (GLRT) detectors, resulting from different cases of prior spectral information. We show that, for all cases of known/unknown source and/or interference power spectrum, the GLRT detectors are, as expected, null steering systems. The depth and shape of the null, as well as the postbeamforming temporal filter, are different and are functions of the a priori known spectrum. Under the assumption that all signals and noise are zero-mean Gaussian processes, we analyze the performance of the different detectors and we exploit their dependency on the array beampattern, as well as on the source and interference signal-to-noise ratio. This analysis serves to identify scenarios where the use of prior spectral information leads to significant performance improvement. >

A CFAR adaptive matched filter detector

An adaptive algorithm for radar target detection using an antenna array is proposed. The detector is derived in a manner similar to that of the generalized likelihood-ratio test (GLRT) but contains a simplified test statistic that is a limiting case of the GLRT detector. This simplified detector is analyzed for performance to signals on boresight, as well as when the signal direction is misaligned with the look direction.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Improved active sonar detection using autoregressive prewhiteners

An active sonar receiver is proposed that more closely approximates the optimal prewhitener and matched filter receiver than currently existing active sonars. The prewhitener is based on an autoregressive model for the reverberation that is shown through analysis of real data to be an accurate model. At the output of the prewhitener a generalized likelihood ratio test detector is implemented. Experimental range-Doppler maps of real data demonstrate that low-Doppler targets are more easily detected than with current FFT processors.