Application of Bayesian multistatic localization to sea trial data

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Authors
  1. Peters, D.J.
Corporate Authors
Defence Research and Development Canada, Atlantic Research Centre, Halifax NS (CAN)
Abstract
Four methods were used to localize an underwater target, using multistatic sonar data from a sea trial that was conducted at the Atlantic Undersea Test and Evaluation Center in February 2013. The first method is a conventional single-ping localization method. The second is a Kalman filter with the single-ping estimates of the first method used as input. The third is a relatively new single-ping localization method that consists mainly of a numerical search for a peak in a probability distribution function. This function is constructed using Bayesian principles. The fourth method is the “Bayesian pseudo-Kalman filter”, which makes its first appearance in this report. It consists of modifying the Bayesian single-ping method by prior information derived from previous estimates that are interpreted as in a Kalman filter. For this data set, the results of the Bayesian methods are markedly superior to those of the more conventional methods, in terms of localization accuracy.

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Keywords
Localization;multistatic active sonar;sensor fusion;target tracking
Report Number
DRDC-RDDC-2016-R245 — Scientific Report
Date of publication
01 Dec 2016
Number of Pages
32
DSTKIM No
CA044534
CANDIS No
805092
Format(s):
Electronic Document(PDF)

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