Fast algorithm of the ML estimator for passive synthetic arrays

Authors:
Yunshan Hou;Dezhi Han
Affiliations:
School of Mathematics and Computational Science, Zhanjiang Normal University, Zhanjiang, China and College of Marine Engineering, Northwestern Polytechnical University, Xi'an, China;School of Information, Guangdong University of Foreign Studies, Guangzhou, China
Venue:
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
Year:
2009

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Abstract

To reduce the heavy computation load of maximum likelihood bearing estimator for passive synthetic arrays(pasaML), a fast algorithm is proposed. This method combines Gibbs sampling with pasaML method, resulting in a frequency-azimuth joint estimation method(called Gibbs-pasaML) to estimate the frequencies and directions of multiple sources at the same time. The method regards the power of pasaML spectrum function as target distribution up to a constant of proportionality, and uses Gibbs sampling technique to sample from it. Simulations show that the new method not only keeps the high-resolution performance of pasaML method but also reduces the computation and storage costs when the number of signal sources is small.