JOURNAL ARTICLE

Maneuvering target tracking using multiple maneuver model joint probabilistic data association

Abstract

The JDPA (joint probabilistic data association) algorithm has been reported to be suitable for tracking multiple targets in the presence of extraneous observations (i.e., clutter, thermal false alarms, other targets) and no reports (missed target detections). In the present work, a novel method for highly maneuvering target tracking is presented which is an extension of the JPDA. Computer simulation indicated that this method can track a highly maneuvering target (7 g) in the presence of extremely dense clutter (with a probability of a false alarm of 0.1) with a sampling interval of 0.5 s.< >

Keywords:
Clutter Probabilistic logic Computer science Data association Tracking (education) Artificial intelligence Joint (building) Track-before-detect False alarm Computer vision Association (psychology) Constant false alarm rate Radar tracker Interval (graph theory) Radar Mathematics Engineering

Metrics

13
Cited By
4.09
FWCI (Field Weighted Citation Impact)
7
Refs
0.95
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Topics

Target Tracking and Data Fusion in Sensor Networks
Physical Sciences →  Computer Science →  Artificial Intelligence
Cognitive Science and Education Research
Life Sciences →  Neuroscience →  Cognitive Neuroscience
Advanced Statistical Methods and Models
Physical Sciences →  Mathematics →  Statistics and Probability

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