The Shifted Rayleigh Mixture Filter for Bearings-only Tracking of Manoeuvering Targets
File(s)
Published version
Author(s)
Clark, JMC
Robbiati, SA
Vinter, RB
Type
Journal Article
Abstract
This paper introduces the shifted Rayleigh mixture filter (SRMF), which is based on jump Markov linear systems. The formulation permits the presence of clutter. For bearings-only tracking problems involving maneuvering targets, the conditional density of the target state given the available measurements evolves as a growing mixture of probability density functions associated with a history of manoeuvre "modes." Similar to other "mixture" algorithms, the SRMF approximates this conditional density by a Gaussian mixture of fixed order. Unlike the extended or unscented Kalman filters,, the shifted Rayleigh filter incorporates an exact calculation of the posterior density, when the prior is assumed to be Gaussian, given the latest bearings measurement. Computer simulations are provided to demonstrate the performance of the algorithm.
Version
Published version
Date Issued
2006
Citation
IEEE Trans Signal Processing, 2006, 55 (7), pp.3218-3226
ISSN
1053-587X
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Start Page
3218
End Page
3226
Journal / Book Title
IEEE Trans Signal Processing
Volume
55
Issue
7
Copyright Statement
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Source Volume Number
55
Subjects
bearings-only tracking
Gaussian mixture reduction
jump Markov linear models
mixture algorithms
particle filter (PF)
shifted Rayleigh filter
unscented Kalman filter
PARTICLE FILTERS
STATE ESTIMATION
SYSTEMS