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Open AccessArticle

A New Multiple Hypothesis Tracker Integrated with Detection Processing

1
School of Electronics & Information Engineering, Beihang University, Beijing 100191, China
2
Department of Engineering, University of Cambridge, Cambridge CB12PZ, UK
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(23), 5278; https://doi.org/10.3390/s19235278
Received: 5 November 2019 / Revised: 25 November 2019 / Accepted: 28 November 2019 / Published: 30 November 2019
(This article belongs to the Special Issue Sensors Localization in Indoor Wireless Networks)
In extant radar signal processing systems, detection and tracking are carried out independently, and detected measurements are utilized as inputs to the tracking procedure. Therefore, the tracking performance is highly associated with detection accuracy, and this performance may severely degrade when detections include a mass of false alarms and missed-targets errors, especially in dense clutter or closely-spaced trajectories scenarios. To deal with this issue, this paper proposes a novel method for integrating the multiple hypothesis tracker with detection processing. Specifically, the detector acquires an adaptive detection threshold from the output of the multiple hypothesis tracker algorithm, and then the obtained detection threshold is employed to compute the score function and sequential probability ratio test threshold for the data association and track estimation tasks. A comparative analysis of three tracking algorithms in a clutter dense scenario, including the proposed method, the multiple hypothesis tracker, and the global nearest neighbor algorithm, is conducted. Simulation results demonstrate that the proposed multiple hypothesis tracker integrated with detection processing method outperforms both the standard multiple hypothesis tracker algorithm and the global nearest neighbor algorithm in terms of tracking accuracy. View Full-Text
Keywords: multiple hypothesis tracker; adaptive detection threshold; score function; sequential probability ratio test multiple hypothesis tracker; adaptive detection threshold; score function; sequential probability ratio test
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Wang, Z.; Sun, J.; Li, Q.; Ding, G. A New Multiple Hypothesis Tracker Integrated with Detection Processing. Sensors 2019, 19, 5278.

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