Robust adaptive signal processing methods for heterogeneous radar clutter scenarios

Rangaswamy, Muralidhar; Lin, Feng; Gerlach, K.

doi:10.1016/j.sigpro.2004.05.006

Cited by 102 publications

(10 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The data is modeled as a sum of low-rank CG sources embedded in white Gaussian noise. This formulations is useful to model clutter (or power-fluctuating sources) plus thermal noise in several array processing applications, such as RADAR [18,19,[21][22][23] . For this model, the samples { y k } K k =1 (the columns of Y ) are drawn as:…”

Section: Data Modelmentioning

confidence: 99%

“…For fixed U t+1 , the update of the eigenvalues and the texture is equivalent to solve respectively the ML problem (19) and ( 17) since these parameters are unknown deterministic (cf. remark 2).…”

Section: Algorithm Derivationmentioning

confidence: 99%

“…Hence, the considered model can accurately describe clutter (or power-fluctuating sources) plus thermal noise observations, which are common in plethora of signal processing application. As an example, this model has been used for detection in heterogeneous environment [18,19] and for robust structured covariance matrix estimation in [15,20] . Specifically, concerning the subspace estimation problem [21,22] , proposed MLE algorithms for this context, and estimation bounds were derived in [23] .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Bayesian signal subspace estimation with compound Gaussian sources

et al. 2020

View full text Add to dashboard Cite

In this paper, we consider the problem of low dimensional signal subspace estimation in a Bayesian context. We focus on compound Gaussian signals embedded in white Gaussian noise, which is a realistic modeling for various array processing applications. Following the Bayesian framework, we derive two algorithms to compute the maximum a posteriori (MAP) estimator and the so-called minimum mean square distance (MMSD) estimator, which minimizes the average natural distance between the true range space of interest and its estimate. Such approaches have shown their interests for signal subspace estimation in the small sample support and/or low signal to noise ratio contexts. As a byproduct, we also introduce a generalized version of the complex Bingham Langevin distribution in order to model the prior on the subspace orthonormal basis. Finally, numerical simulations illustrate the performance of the proposed algorithms.

show abstract

Section: Data Modelmentioning

confidence: 99%

Section: Algorithm Derivationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Bayesian signal subspace estimation with compound Gaussian sources

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Therefore, a rank can be considered as an output of the FML. The FML's success in STAP is widely known [21]. • RCML EL : The RCML estimator with the rank obtained by the proposed rank selection method.…”

Section: Experimental Validationmentioning

confidence: 99%

Automatic rank estimation for practical STAP covariance estimation via an expected likelihood approach

Kang

Monga

Rangaswamy

et al. 2015

2015 IEEE Radar Conference (RadarCon)

Self Cite

View full text Add to dashboard Cite

We address the problem of estimation of structured covariance matrices for radar space-time adaptive processing (STAP) 1 . The knowledge of the interference environment has been exploited in many previous works to accurately estimate a structured disturbance covariance matrix. In particular, it has been shown that employing the rank of clutter subspace, i.e. rank constrained maximum likelihood (RCML) estimation, leads to a practically powerful estimator as well as a closed form solution. While the rank is a very effective constraint, often practical non-idealities make it difficult to be known precisely using physical models. We propose an automatic rank estimation method in STAP via an expected likelihood (EL) approach. We formulate rank estimation as an optimization problem with the expected likelihood criterion and formally prove that the proposed optimization has a unique solution. Through experimental results from a simulation model and KASSPER dataset, we show the RCML estimator with the rank obtained via the EL approach outperforms RCML estimators with the other rank selection methods in the sense of a normalized signalto-interference and noise ratio (SINR) and the probability of detection.

show abstract

“…The normalized matched filter (NMF) test for the problem of detecting a rank one signal in additive clutter has been derived in numerous references cited in [ I , 21. The NMF test is given by where e is the known spatio-temporal signal steering vector, and R, is the known clutter covariance matrix.…”

Section: Introductionmentioning

confidence: 99%

Normalized adaptive matched filter - a low rank approach

Rangaswamy

Lin²

Proceedings of the 3rd IEEE International Symposium on Signal Processing and Information Technology (IEEE Cat. No.03EX795)

View full text Add to dashboard Cite

This paper presents the performance of the normalized matched filter (NMF) test in a background of disturbance consisting of clutter having a covariance matrix with known structure and unknown scaling plus background white Gaussian noise. It is shown that when the clutter covariance matrix is low rank, the NMF test retains invariance with respect to the unknown scaling as well as the background noise level and is approximately CFAR. Analytical expressions for calculating the false a l m and detection probabilities are presented. Performance of the method is shown to degrade with increasing clutter rank especially for low false alarm rates. An adaptive version of the iest is developed and its performance is studied with simulated data from the KASSPER program. Issues of sample support for subspace estimation, constant false alarm rate (CFAR) and detection performance are presented.

show abstract

Robust adaptive signal processing methods for heterogeneous radar clutter scenarios

Cited by 102 publications

References 41 publications

Bayesian signal subspace estimation with compound Gaussian sources

Bayesian signal subspace estimation with compound Gaussian sources

Automatic rank estimation for practical STAP covariance estimation via an expected likelihood approach

Normalized adaptive matched filter - a low rank approach

Contact Info

Product

Resources

About