Knowledge-Aided Bayesian Radar Detectors &amp; Their Application to Live Data

Maio, Antonio De; Farina, A.; Foglia, Goffredo

doi:10.1109/taes.2010.5417154

Cited by 114 publications

(56 citation statements)

References 25 publications

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“…Alternative ways of detecting multiple targets in cluttergoverned environments have been presented in the literature. As an example, Bayesian [12,13] and artificial neural networks [14,15] techniques have been successfully used, considering a single-scan strategy approach. As pointed out in [16], the posterior distribution of multiple target state is a multi-mode distribution and each mode corresponds to either a target or clutter.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Scan Mixture Particle Filter for Joint Detection and Tracking of Multiple Targets

Liu¹,

Han²,

Yao³

et al. 2013

PIER B

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Abstract-In this paper, a novel algorithm named multi-scan mixture particle filter is proposed for joint detection and tracking for a varying number of targets. The posterior distribution of multiple target state in a single-target state space is a multi-mode distribution with each mode corresponding to either a target or clutter. A general global posterior distribution is adopted in this work, which consists of existing components and new components. The new components are generated at each time step to capture the new modes due to newly appeared targets or clutter. In order to distinguish targets from clutter, multiple scan information is incorporated. The history of each component's associate weights is stored in a multi-scan sliding window, which is used to judge whether the component is from a target or clutter. Moreover, a novel sampling method which combines the likelihood sampling and prior sampling is proposed to draw particles from the desired parts of the state space at each time step. From the simulation results, it could be seen that the proposed algorithm can effectively detect the appearance/disappearance of the targets as well as track the existing target.

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Section: Introductionmentioning

confidence: 99%

A Multi-Scan Mixture Particle Filter for Joint Detection and Tracking of Multiple Targets

Liu¹,

Han²,

Yao³

et al. 2013

PIER B

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“…Recently, a Bayesian approach to the detection problem emerged [3], [4], where the covariance matrix is assumed to be randomly distributed with some prior distribution. The resulting detectors are often referred to as knowledgeaided (KA) detectors for the stochastic homogeneous environment.…”

Section: Introductionmentioning

confidence: 99%

“…The resulting detectors are often referred to as knowledgeaided (KA) detectors for the stochastic homogeneous environment. Using both measured L-band clutter data and highfidelity KASSPER data, the KA detectors were shown to have improved performance than the conventional detectors when the homogeneous training signals are limited [4]. For non-homogeneous environments, several models have been proposed.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, we assume the covariance matrix R to be random and has a complex inverse Wishart distribution, i.e., R ∼ CW −1 ((µ − N )R, µ) [3], [4], [9]:…”

Section: Introductionmentioning

confidence: 99%

“…If λ = 1, the stochastic partially homogeneous model reduces to the stochastic homogeneous model [3], [4]. According to the stochastic partially homogeneous model, the scale-invariant generalized likelihood ratio test (GLRT) is developed within a Bayesian framework.…”

Section: Introductionmentioning

confidence: 99%

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Knowledge-Aided Adaptive Coherence Estimator in Stochastic Partially Homogeneous Environments

Wang

Şahinoğlu

Pun

et al. 2011

IEEE Signal Process. Lett.

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This paper introduces a stochastic partially homogeneous model for adaptive signal detection. In this model, the disturbance covariance matrix of training signals, R, is assumed to be a random matrix with some a priori information, while the disturbance covariance matrix of the test signal, R0, is assumed to be equal to R, i.e., R0 = R. On one hand, this model extends the stochastic homogeneous model by introducing an unknown power scaling factor between the test and training signals. On the other hand, it can be considered as a generalization of the standard partially homogeneous model to the stochastic Bayesian framework, which treats the covariance matrix as a random matrix. According to the stochastic partially homogeneous model, a scale-invariant generalized likelihood ratio test (GLRT) for the adaptive signal detection is developed, which is a knowledge-aided version of the well-known adaptive coherence estimator (ACE). The resulting knowledge-aided ACE (KA-ACE) employs a colored loading step utilizing the a priori knowledge and the sample covariance matrix. Various simulation results and comparison with respect to other detectors confirm the scale-invariance and the effectiveness of the KA-ACE. IEEE Signal Processing LettersThis work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved.

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