Comparison of particle filter and histogram filter performance for passive sonar localization

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Abstract

This paper compares the performance of histogram and particle filters for localizing and tracking a highly maneuvering target using two widely spaced horizontal passive sonar arrays. Both filters are numerical implementations of Bayes' filter, which use recursively estimate state vectors with nonlinear update equations and non-Gaussian prior probability density functions. The histogram filter uses a grid-based approach that is analogous to midpoint rectangular integration, while the particle filter uses a direct Monte Carlo approach. Both filters are shown to successfully track a source in an example with synthetic data given sufficient computational resources. Their performance is also compared in situations where computational power is severely restricted; then the particle filter outperforms the histogram filter in this example.

Original languageEnglish (US)
Article number055001
JournalProceedings of Meetings on Acoustics
Volume8
DOIs
StatePublished - Dec 1 2010
Event158th Meeting Acoustical Society of America 2009 - San Antonio, TX, United States
Duration: Oct 26 2009Oct 30 2009

All Science Journal Classification (ASJC) codes

  • Acoustics and Ultrasonics

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