Sequential Monte Carlo methods for shallow water tracking using multiple sensors with adaptive frequency selection

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

We propose a matched-field processing framework for tracking problems in shallow water environments where the conventional plane-wave assumptions do not hold. Multiple passive acoustic sensors are employed to collect observation data, and sequential Monte Carlo techniques are used for tracking due to the high nonlinearity in the dynamic state formulation. In order to enhance the tracking performance, we design a frequency selection algorithm which adaptively chooses the optimal observation frequency for the sensors at each time instant. The improved tracking performance is demonstrated using simulations.

Original languageEnglish (US)
Title of host publication2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP
Pages269-272
Number of pages4
DOIs
StatePublished - Dec 1 2007
Event2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP - St. Thomas, Virgin Islands, U.S.
Duration: Dec 12 2007Dec 14 2007

Publication series

Name2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP

Other

Other2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP
CountryVirgin Islands, U.S.
CitySt. Thomas
Period12/12/0712/14/07

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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