Acoustic Channel Tracking with the Cardinalized Probability Hypothesis Density Filter and the Multiple Hypothesis Tracker
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چکیده
Two datasets, one simplistic that assumes direct observation of paths and the other based on observations derived from compressed sensing and an assumed OFDM communications underpinning, simulate underwater acoustic channels. The Cardinalized Probability Hypothesis Density filter and the Multiple Hypothesis Tracker are applied to these wireless channels. The performances of the two trackers are evaluated and compared using the Optimal Subpattern Assignment metric.
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تاریخ انتشار 2010