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Article
Bayesian Complex Amplitude Estimation and Adaptive Matched Filter Detection in Low-Rank Interference
IEEE Transactions on Signal Processing
  • Aleksandar Dogandžić, Iowa State University
  • Benhong Zhang, Iowa State University
Document Type
Article
Disciplines
Publication Date
3-1-2007
DOI
10.1109/TSP.2006.887151
Abstract
We propose a Bayesian method for complex amplitude estimation in low-rank interference. We assume that the received signal follows the generalized multivariate analysis of variance (GMANOVA) patterned-mean structure and is corrupted by low-rank spatially correlated interference and white noise. An iterated conditional modes (ICM) algorithm is developed for estimating the unknown complex signal amplitudes and interference and noise parameters. We also discuss initialization of the ICM algorithm and propose a (non-Bayesian) adaptive-matched-filter (AMF) signal detector that utilizes the ICM estimation results. Numerical simulations demonstrate the performance of the proposed methods
Comments

This is a post-print of an article from IEEE Transactions on Signal Processing 55 (2007): 1176–1182, doi:10.1109/TSP.2006.887151. Posted with permission.

Rights
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Copyright Owner
IEEE
Language
en
File Format
application/pdf
Citation Information
Aleksandar Dogandžić and Benhong Zhang. "Bayesian Complex Amplitude Estimation and Adaptive Matched Filter Detection in Low-Rank Interference" IEEE Transactions on Signal Processing Vol. 55 Iss. 32 (2007) p. 1176 - 1182
Available at: http://works.bepress.com/aleksandar_dogandzic/4/