NDLI: An algorithm for tracking multiple targets

Content Provider	IEEE Xplore Digital Library
Author	Reid, D.B.
Copyright Year	1978
Description	Author affiliation: Lockheed Palo Alto Research Laboratory, Palo Alto, CA (Reid, D.B.)
Abstract	An algorithm for tracking multiple targets in a cluttered environment is developed. The algorithm is capable of initiating tracks, accounting for false or missing reports, and processing sets of dependent reports. As each measurement is received, probabilities are calculated for the hypotheses that the measurement came from previously known targets in a target file, or from a new target, or that the measurement is false. Target states are estimated from each such data-association hypothesis, using a Kalman filter. As more measurements are received, the probabilities of joint hypotheses are calculated recursively using all available information such as density of unknown targets, density of false targets, probability of detection, and location uncertainty. This branching technique allows correlation of a measurement with its source based on subsequent, as well as previous, data. To keep the number of hypotheses reasonable, unlikely hypotheses are eliminated and hypotheses with similar target estimates are combined. To minimize computational requirements, the entire set of targets and measurements is divided into clusters that are solved independently. In an illustrative example of aircraft tracking, the algorithm successfully tracks targets over a wide range of conditions.
Starting Page	1202
Ending Page	1211
File Size	1043641
Page Count	10
File Format	PDF
DOI	10.1109/CDC.1978.268125
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1979-01-10
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Target tracking Sea measurements Probability Clustering algorithms Radar tracking Military aircraft Air traffic control Radar detection Military computing Land vehicles
Content Type	Text
Resource Type	Article

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