Gaussian Sum Shifted Rayleigh Filter for Underwater Bearings-Only Target Tracking Problems

In this paper, a new Gaussian sum (GS) filter with shifted Rayleigh filter (SRF) as proposal has been developed for the bearings-only tracking (BOT) problem. The proposed filter estimates the prior and posterior probability density functions as weighted sum of several Gaussian densities, where the i...

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Bibliographic Details
Published in:IEEE journal of oceanic engineering 2019-04, Vol.44 (2), p.492-501
Main Authors: Radhakrishnan, Rahul, Bhaumik, Shovan, Tomar, Nutan Kumar
Format: Article
Language:English
Subjects:
Bearing (direction)
Bearings-only tracking (BOT)
Computer simulation
Computing time
Conditional probability
Covariance
Density measurement
Divergence
Filters
Kalman filters
Lower bounds
Mathematical model
Noise measurement
nonlinear filtering
Nonlinear filters
Observers
Probability density functions
Probability theory
Root-mean-square errors
Sea measurements
state estimation
Target tracking
Tracking
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Description
Summary:In this paper, a new Gaussian sum (GS) filter with shifted Rayleigh filter (SRF) as proposal has been developed for the bearings-only tracking (BOT) problem. The proposed filter estimates the prior and posterior probability density functions as weighted sum of several Gaussian densities, where the individual Gaussian densities are generated from the SRF. Performance of the proposed filter on the BOT problem is compared with the SRF, the GS sparse-grid Gauss-Hermite filter, and other available GS and quadrature-based filters. Performance of filters is compared in terms of a root-mean-square error (RMSE), track divergence, and computational time. The effect of initial uncertainty, measurement noise covariance, and sampling time on filtering accuracy are also studied. Finally, RMSEs of all the filters are evaluated in comparison with the Cramer-Rao lower bound. From simulation results, it is observed that the performance of the proposed GS-SRF is superior to all other nonlinear filters considered.
ISSN:0364-9059
1558-1691
DOI:10.1109/JOE.2018.2814218