Paper
28 July 1997 Improved multiple target tracking using Dempster-Shafer identification
Henry Leung, Yifeng Li, Eloi Bosse, Martin Blanchette, Keith C. C. Chan
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Abstract
Tracking multiple maneuvering targets in clutter is a challenging problem. Using only the measured kinematic quantities is usually not adequate to meet the requirements on a multiple target tracking (MTT) system, i.e., to partition the sensor data into tracks of the targets while suppressing the clutter and false alarms. The efficient use of attribute data in addition to the kinematic measurements can greatly enhance the capability of an MTT system in discrimination against the false tracks. In this paper, the friend-foe identification information, the target run-length which measures the number of hits by a radar for a target, and the estimated target speed at each update of a track are used for true and false track identification. Since not all the information are available for all the tracks at all time, Dempster-Shafer's evidential reasoning is employed to combine these pieces of uncertain information with different levels of abstraction. Real air surveillance radar data were collected to evaluate the effectiveness of this combined tracking and identification approach. Results shows that the fusion of track attribute data with the kinematic estimates by Dempster-Shafer reasoning provides very satisfactory discrimination between the true and false tracks, thus greatly improves the system's surveillance capability over the system that uses only the kinematic data.
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Henry Leung, Yifeng Li, Eloi Bosse, Martin Blanchette, and Keith C. C. Chan "Improved multiple target tracking using Dempster-Shafer identification", Proc. SPIE 3068, Signal Processing, Sensor Fusion, and Target Recognition VI, (28 July 1997); https://doi.org/10.1117/12.280800
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Cited by 7 scholarly publications.
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KEYWORDS
Radar

Surveillance

Target recognition

Kinematics

Sensors

Detection and tracking algorithms

Surveillance systems

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