Persymmetric Parametric Adaptive Matched Filter for Multichannel Adaptive Signal Detection

This correspondence considers a parametric approach for multichannel adaptive signal detection in Gaussian disturbance which can be modeled as a multichannel autoregressive (AR) process and, moreover, possesses a persymmetric structure induced by a symmetric antenna geometry. By introducing the pers...

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Veröffentlicht in:IEEE transactions on signal processing 2012-06, Vol.60 (6), p.3322-3328
Hauptverfasser: Pu Wang, Sahinoglu, Zafer, Man-On Pun, Hongbin Li
Format: Artikel
Sprache:eng
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Zusammenfassung:This correspondence considers a parametric approach for multichannel adaptive signal detection in Gaussian disturbance which can be modeled as a multichannel autoregressive (AR) process and, moreover, possesses a persymmetric structure induced by a symmetric antenna geometry. By introducing the persymmetric AR (PAR) modeling for the disturbance, a persymmetric parametric adaptive matched filter (Per-PAMF) is proposed. The developed Per-PAMF extends the classical PAMF by exploiting the underlying persymmetric properties and, hence, improves the detection performance in training-limited scenarios. The performance of the proposed Per-PAMF is examined by the Monte Carlo simulations and simulation results demonstrate the effectiveness of the Per-PAMF compared with the conventional PAMF and nonparametric detectors.
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2012.2190411