GLRT Detector Research Articles

Knowledge‐aided detection for airborne MIMO radar by exploiting structured clutter spectrum

In this study, the authors propose two knowledge-aided detection schemes based on prior structured clutter information for airborne collocated multiple-input multiple-output (MIMO) radars. Based on a first-order representation of the clutter spectrum, they propose the first-order generalised likelihood ratio test (FO-GLRT) detector. Meanwhile, Based on a second-order representation of the clutter spectrum, they propose the second-order GLRT (SO-GLRT) detector. They also analyse and compare the detection performance of those two detectors in different situations. The FO-GLRT detector can achieve acceptable performance without any secondary data, and thus immune to the clutter heterogeneity. The SO-GLRT detector that exploits the structured clutter covariance matrix is able to achieve a nearly optimal performance in certain cases. Both the proposed detectors are suitable for the collocated MIMO radars, which have a high signal dimension and a large demand for independent identically distributed training samples. Simulations validate those results.

Mismatched Signal Rejection Performance of the Persymmetric GLRT Detector

We analytically evaluate the mismatched signal rejection performance of a persymmetric generalized likelihood ratio test (PGLRT) detector in a mismatched case where the nominal target steering vector is not aligned with the true one. This PGLRT detector can be applied to target detection in Gaussian noise with unknown covariance matrix for several cases where the data under test are collected from multiple range cells (i.e., for a distributed target), multiple bands, or multiple coherent processing intervals. We derive analytical expressions for the detection probability of the PGLRT detector in the presence of target steering vector mismatch. All the theoretical results are verified with Monte Carlo simulations. Numerical examples are provided to show that the PGLRT detector is sensitive to the steering vector mismatches.

Adaptive Two-Step Bayesian Generalized Likelihood Ratio Test Algorithm for Low-Altitude Detection

The low-altitude target detection remains a difficult problem in MIMO radar. In this paper, we propose a novel adaptive two-step Bayesian generalized likelihood ratio test (TB-GLRT) detection algorithm for low-altitude target detection. By defining the compound channel scattering coefficient and applying the K distributed clutter model, the signal models for different radars in low-altitude environment are established. Then, aiming at the problem that the integrals are too complex to yield a closed-form Neyman-Pearson detector, we assume prior knowledge of the channel scattering coefficient and clutter to design an adaptive two-step Bayesian GLRT algorithm for low-altitude target detection. Monte Carlo simulation results verify that the proposed detector has better performance than the square law detector, GLRT detector or Bayesian GLRT detector in low-altitude environment. With the TB-GLRT detector, the maximum detection probability can reach 70% when SNR=0dB and ν=1. Simulations also verify that the multipath effect shows positive influence on detection when SNR<5dB, and when SNR>10dB, the multipath effect shows negative influence on detection. When SNR>0dB, the MIMO radar, which keeps a detection probability over 70% with the proposed algorithm, has the best detection performance. Besides, the detection performance gets improved with the decrease of sea clutter fluctuation level.

An Augmented Generalized Likelihood Ratio Test Detector for Signal Detection in Clutter and Noise

To deal with the problem of signal detection in correlated subspace, this paper presents an augmented generalized likelihood ratio test (AGLRT) detector. Compared with traditional GLRT detector, this AGLRT detector has the advantage of incorporating the intersection component between signal and clutter subspace. Signal-contribution-ratio (SCR) and clutter-contribution-ratio (CCR) are defined to characterize the contribution of signal and clutter in the common subspace, respectively. The closed forms of both probability of detection and false alarm for the AGLRT detector are presented. It is shown that the detection performance of the AGLRT detector is better than the GLRT detector by choosing appropriate weight and threshold parameters. Then, analytical formulas of the suboptimal weight and threshold to maximize the detection performance are obtained. Finally, the effectiveness of the proposed detector is demonstrated by various examples.

GLRT detector based on knowledge aided covariance estimation in compound Gaussian environment

Abstract In order to alleviate the effect of the limited secondary data in the non-Gaussian clutter, a knowledge aided adaptive detector is proposed. The covariance matrix estimation is modeled as a general linear combination of prior covariance matrix and sample covariance matrix. Within this consideration, we obtain an adaptive detector based on the generalized likelihood ratio test. Experimental results on simulation and real data demonstrate that the proposed detector achieves better performance than the existing one-step GLRT (1S-GLRT) detectors when the secondary data are insufficient.

Double Detector for Sparse Signal Detection From One-Bit Compressed Sensing Measurements

This letter presents the sparse vector signal detection from one bit compressed sensing measurements, in contrast to the previous works which deal with scalar signal detection. In this letter, available results are extended to the vector case and the GLRT detector and the optimal quantizer design are obtained. Also, a double-detector scheme is introduced in which a sensor level threshold detector is integrated into network level GLRT to improve the performance. The detection criteria of oracle and clairvoyant detectors are also derived. Simulation results show that with careful design of the threshold detector, the overall detection performance of double-detector scheme would be better than the sign-GLRT proposed in [1] and close to oracle and clairvoyant detectors. Also, the proposed detector is applied to spectrum sensing and the results are near the well known energy detector which uses the real valued data while the proposed detector only uses the sign of the data.

Subspace Compressive GLRT Detector for MIMO Radar in the Presence of Clutter.

The problem of optimising the target detection performance of MIMO radar in the presence of clutter is considered. The increased false alarm rate which is a consequence of the presence of clutter returns is known to seriously degrade the target detection performance of the radar target detector, especially under low SNR conditions. In this paper, a mathematical model is proposed to optimise the target detection performance of a MIMO radar detector in the presence of clutter. The number of samples that are required to be processed by a radar target detector regulates the amount of processing burden while achieving a given detection reliability. While Subspace Compressive GLRT (SSC-GLRT) detector is known to give optimised radar target detection performance with reduced computational complexity, it however suffers a significant deterioration in target detection performance in the presence of clutter. In this paper we provide evidence that the proposed mathematical model for SSC-GLRT detector outperforms the existing detectors in the presence of clutter. The performance analysis of the existing detectors and the proposed SSC-GLRT detector for MIMO radar in the presence of clutter are provided in this paper.

Low-Grazing Angle Detection in Compound-Gaussian Clutter with Hybrid MIMO Radar

This paper focuses on the target detection in low-grazing angle using a hybrid multiple-input multiple-output (MIMO) radar systems in compound-Gaussian clutter, where the multipath effects are very abundant. The performance of detection can be improved via utilizing the multipath echoes. First, the reflection coefficient considering the curved earth effect is derived. Then, the general signal model for MIMO radar is introduced in low-grazing angle; also, the generalized likelihood test (GLRT) and generalized likelihood ratio test-linear quadratic (GLRT-LQ) are derived with known covariance matrix. Via the numerical examples, it is shown that the derived GLRT-LQ detector outperforms the GLRT detector in low-grazing angle, and both performances can be enhanced markedly when the multipath effects are considered.

高分辨雷达扩展目标检测算法研究

The problem of extended target detection for high resolution radar is addressed. After modeling the radar echo signal, the optimal detector in the ideal case, in which all information of the target is known, is derived. The performance of the optimal detector, which gives an upper bound of the detection performance for all potential detectors, is analyzed. Then the GLRT detector is discussed in ve cases in which the information of the target is partially or entirely unknown. The detection performances of these detectors are analyzed, and the theoretical and simulation results con rm that the more information about the target a detector has, the better detection performance it shows. The order of the detection performance of these detectors reveals that the detection performance of the order accumulation detector gives an upper bound of the detection performance for all practical detector for which all the information of the target is completely unknown. A double threshold detector based on scatter number and GLRT(BS-GLRT) is proposed. In the proposed detector, the number of target scatters is estimated by comparing signal amplitudes at all range cells against the rst threshold, and all the amplitudes above the rst threshold are accumulated and compared against the second threshold to make detection decision. Simulation con rms that the proposed detector outperforms the existing methods in all cases and the detector performance is very close to the upper bound.

FDGLRT detector of MIMO radar in non-homogeneous clutter

Recently, an adaptive generalised likelihood ratio test-linear quadratic (AGLRT_LQ) detector has been proposed for MIMO radar in the presence of non-homogeneous clutter. However, this method suffers a large performance loss when the amount of secondary data available is small. In this reported work, a full data GLRT detector is derived by utilising not only the primary data but also the secondary data in the design process and the covariance matrix estimation process to decrease this loss.

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.

A two-dimensional generalized likelihood ratio test for land mine and small unexploded ordnance detection

The fundamental goals of land-mine and small unexploded ordnance (UXO) detection are to achieve a high probability of detection ( P d) and a low probability of false alarm ( P fa). Conventional methods usually fulfill the first goal at the cost of a high P fa. In our previous work (Collins et al., IEEE Trans. Geosci. Remote Sensing 37 (2) (March 1998) 811–819; Gao and Collins, Proceedings of SPIE, Orlando, FL, April 1998; Gao, Master's Thesis, Duke University, December, 1997), we have shown that a Bayesian decision theoretic approach can be applied to improve the detectibility of land mines and small UXO targets using a single spatial sample of the electromagnetic induction (EMI) sensor data. In this paper, we present an alternative approach which significantly improves P d at a fixed P fa by utilizing features that capture the physical nature of EMI data within a statistical signal processing framework. The method we develop is a two-dimensional generalized likelihood ratio test (2-D GLRT) which utilizes spatial information from the sensor output. To illustrate the performance improvement, results obtained with the 2-D GLRT detector are compared to those for the standard threshold test for single-channel time-domain sensor data, as well as the energy detector, the integral detector, and the single location generalized likelihood ratio test (1-D GLRT) detector for multi-channel time-domain EMI sensor data.