TR2012-018

Parametric Multichannel Adaptive Signal Detection: Exploiting Persymmetric Structure


    •  Wang, P.; Sahinoglu, Z.; Pun, M.-O.; Li, H., "Parametric Multichannel Adaptive Signal Detection: Exploiting Persymmetric Structure", IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), March 2012.
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      • @inproceedings{Wang2012mar2,
      • author = {Wang, P. and Sahinoglu, Z. and Pun, M.-O. and Li, H.},
      • title = {Parametric Multichannel Adaptive Signal Detection: Exploiting Persymmetric Structure},
      • booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
      • year = 2012,
      • month = mar,
      • url = {http://www.merl.com/publications/TR2012-018}
      • }
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  • Research Area:

    Signal Processing


This paper considers a parametric approach for adaptive multichannel signal detection, where the disturbance is modeled by a multichannel auto-regressive (AR) process. Motivated by the fact that a symmetric antenna geometry usually yields a per-symmetric structure on the covariance matrix of disturbance, a new per-symmetric AR (PAR) modeling for the disturbance is proposed and, accordingly, a per-symmetric parametric adaptive matched filter (Per-PAMF) is developed. The developed Per-PAMF, while allowing a simple implementation like the traditional PAMF, extends the PAMF by developing the maximum likelihood (ML) estimation of unknown nuisance (disturbance-related) parameters under the per-symmetric constraint. Numerical results show that the Per-PAMF provides significantly better detection performance than the conventional PAMF and other non-parametric detectors when the number of training signals is limited.