On Using a priori Knowledge in Space-Time Adaptive Processing

Stoica, Peter; Li, Jian; Zhu, Xiaojun; Guerci, J.R.

doi:10.1109/tsp.2007.914347

Cited by 214 publications

(197 citation statements)

References 6 publications

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“…DL Techniques: These are based on regularizing the SCM by adding a positive real number to its diagonal, yielding the next expression for the so called DL-LMMSE or DL-MVDR filters respectively, (11) (12) Note that the key point is how to choose the regularization parameter . A lot of research has been devoted to this end, e.g., in array processing.…”

Section: E Related Workmentioning

confidence: 99%

“…For the complex case, according to [29], one can stack the real and imaginary parts of the data, then estimate the associated covariance using the LW method and finally obtain the complex covariance from it, though this may give suboptimal performance as the circular symmetry property of complex data is not used. [12], [13] and references therein. In any case, all these methods aim to improve the estimation of the SCM and they do not deal directly with the estimation of the parameter of interest.…”

Section: E Related Workmentioning

confidence: 99%

“…In fact, DL based techniques can be regarded as a particular case of linear shrinkage estimators of the SCM. Namely, the shrinkage estimator, , of the correlation of the observations, , reads , see e.g., [11], [12] or [13], where may be simply the identity matrix or an estimator with certain bias but very low estimation variance which may be obtained from a priori knowledge stemming from the problem at hand. The idea of those estimators is to introduce some bias in the estimation such that the overall estimation error is diminished compared to the one of the SCM.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, in constrast to some rather ad hoc choices in DL, e.g., ([5], p. 748), an analytical expression for a class of asymptotically optimal filters, in terms of MSE, is presented with an improvement in performance. Furthermore, unlike other DL techniques [14] or methods that shrink the SCM, e.g., [11], [12] or [13], we seek to optimize directly the MSE of the parameter of interest instead of enhancing the estimation of the covariance. We make use of this fact to obtain estimators with a performance improvement.…”

Section: Introductionmentioning

confidence: 99%

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Asymptotically Optimal Linear Shrinkage of Sample LMMSE and MVDR Filters

Serra

Nájar

2014

IEEE Trans. Signal Process.

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Abstract-Conventional implementations of the linear minimum mean-square (LMMSE) and minimum variance distortionless response (MVDR) estimators rely on the sample matrix inversion (SMI) technique, i.e., on the sample covariance matrix (SCM). This approach is optimal in the large sample size regime. Nonetheless, in small sample size situations, those sample estimators suffer a large performance degradation. Thus, the aim of this paper is to propose corrections of these sample methods that counteract their performance degradation in the small sample size regime and keep their optimality in large sample size situations. To this aim, a twofold approach is proposed. First, shrinkage estimators are considered, as they are known to be robust to the small sample size regime. Namely, the proposed methods are based on shrinking the sample LMMSE or sample MVDR filters towards a variously called matched filter or conventional (Bartlett) beamformer in array processing. Second, random matrix theory is used to obtain the optimal shrinkage factors for large filters. The simulation results highlight that the proposed methods outperform the sample LMMSE and MVDR. Also, provided that the sample size is higher than the observation dimension, they improve classical diagonal loading (DL) and Ledoit-Wolf (LW) techniques, which counteract the small sample size degradation by regularizing the SCM. Finally, compared to state-of-the-art DL, the proposed methods reduce the computational cost and the proposed shrinkage of the LMMSE obtains performance gains.

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Section: E Related Workmentioning

confidence: 99%

Section: E Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Asymptotically Optimal Linear Shrinkage of Sample LMMSE and MVDR Filters

Serra

Nájar

2014

IEEE Trans. Signal Process.

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“…Therefore, aiming at the problem of the nonhomogeneous environment and large computation burden of fully adaptive space time processing, some typical suboptimal STAP approaches were proposed [31][32][33][34][35][36][37]. In the end of 1990s, the knowledgebased space-time adaptive processing (KB-STAP) for airborne early warning radar was proposed, which was the hot topic in the first ten years of the 21st century [38][39][40]. However, the STAP's performance becomes significantly degradation when the target and clutter are similar to each other in the angle domain and Doppler domain or when the knowledges do not agree with the reality in KB-STAP.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Polarization-Space-Time Three-Domain Joint Processing for Clutter Suppression in Airborne Radar

Di-jun¹,

Xu²,

Zhang³

et al. 2012

PIER

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Abstract-An optimum polarization-space-time joint domain processing (PST-JDP) technique is proposed for clutter suppression which adequately adopts the three-domain information including the polarization, space and Doppler frequency information of the radar echo. The study shows that the polarization information together with the space and Doppler frequency information are effective to significantly enhance the clutter suppression performance for airborne radar. Several new techniques, (i.e., the covariance matrix eigendecomposition, the spectral analysis and the resolution grid method), are utilized for deriving the performance of the optimum PST-JDP. The main factors which affect on the performance of clutter rejection are the clutter degree of polarization, statistical distance of polarization between target and clutter, Doppler frequency of target and input clutter-to-noise ratio. The new optimum PST-JDP method outperforms significantly the traditional optimum space-time processing technology, especially in the case of the slowly or tangentially moving target. The simulation verifies the correctness and efficiency of the model.

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Robust Adaptive Beamforming

2018

Simplified Robust Adaptive Detection and Beamforming for Wireless Communications

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On Using a priori Knowledge in Space-Time Adaptive Processing

Cited by 214 publications

References 6 publications

Asymptotically Optimal Linear Shrinkage of Sample LMMSE and MVDR Filters

Asymptotically Optimal Linear Shrinkage of Sample LMMSE and MVDR Filters

Performance Analysis of Polarization-Space-Time Three-Domain Joint Processing for Clutter Suppression in Airborne Radar

Robust Adaptive Beamforming

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