NDLI: Concurrent Detection and Tracking using Multiple, Flying, Sensors

Content Provider	IEEE Xplore Digital Library
Author	Deming, R. Perlovsky, L.
Copyright Year	2006
Description	Author affiliation: Anteon Corp., Fairfax, VA (Deming, R.)
Abstract	We develop a probabilistic technique for performing multiple target detection and tracking based on data from multiple, flying, sensors. Multiple sensors can facilitate detecting and discriminating low signal-to-clutter targets by allowing correlation between different sensor types and/or different aspect angles. However, the data association problem can cause the computational complexity of standard trackers to become prohibitively high when combining too much data - a problem which will be exacerbated when including data from multiple sensors. Dynamic logic (DL) is a probabilistic technique for performing data association, based upon maximum likelihood parameter estimation of mixture models, which does not suffer from a computational explosion with increasing amounts of data. Previously, a DL-based tracker was developed for the relatively simple case incorporating data from a stationary sensor platform. In this paper we expand the framework to incorporate multiple, moving, sensor platforms, which requires a revision in the parameter estimation equations. The framework is general enough to be valid for different sensor types, for example radar or electro-optical. Sample results from synthetic data are presented
Starting Page	505
Ending Page	509
File Size	209659
Page Count	5
File Format	PDF
ISBN	1424403081
DOI	10.1109/SAM.2006.1706185
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Radar tracking Target tracking Parameter estimation Object detection Computational complexity Probabilistic logic Maximum likelihood detection Maximum likelihood estimation Explosions Equations
Content Type	Text
Resource Type	Article

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