NDLI: Spectral feature-aided multi-target multi-sensor passive sonar tracking

Content Provider	IEEE Xplore Digital Library
Author	Pace, D.W. Mallick, M. Eldredge, W.
Copyright Year	2003
Description	Author affiliation: Lockheed Martin Orincon, San Diego, CA, USA (Pace, D.W.; Mallick, M.; Eldredge, W.)
Abstract	Passive sonar systems typically provide target bearing estimates that are a nonlinear function of the target state. Multiple state Gaussian Sum extended Kalman filter (EKF) and particle filter (PF) approaches have been combined in a previous multiple hypothesis tracking (MHT) architecture to improve state estimation on bearings-only data. Bearing-only measurements also introduce difficulties in data association as a result of uncertainties, ambiguities, and multiplicity of contacts on a common bearing. In this paper, we shall present an approach to improving data association and filtering by exploiting passive narrowband (PNB) spectral features. The paper identifies the PNB spectral feature target state extension, introduces a Gaussian sum frequency state model, and defines extensions to the likelihood calculations needed for improved data fusion across both kinematic and frequency domains. The track likelihood is based on both kinematic and spectral feature likelihoods. Frequency domain fusion extensions are shown to fit seamlessly into a current MHT architecture.
Sponsorship	Dept. of the Navy Sci. and Technol. Coalition for Enhanced Marine Resources Orincon Ceros Harbour Offshore Inc
Starting Page	2120
Ending Page	2126
File Size	482082
Page Count	7
File Format	PDF
ISBN	0933957300
DOI	10.1109/OCEANS.2003.178230
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-09-22
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	MTS
Subject Keyword	Target tracking State estimation Kinematics Frequency domain analysis Sonar measurements Particle filters Particle tracking Passive filters Filtering Narrowband
Content Type	Text
Resource Type	Article

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