Multi-frame assignment PMHT that accounts for missed detections

Author

Blanding, Wayne R. ; Willett, Peter ; Streit, Roy L. ; Dunham, Darin

Author_Institution

Dept. of Electr. & Comp. Eng., Connecticut Univ., Storrs, CT

fYear

2007

fDate

9-12 July 2007

Firstpage

1

Lastpage

8

Abstract

Probabilistic multi-hypothesis tracking (PMHT) is an algorithm for tracking multiple targets when measurement-to- target assignments are unknown and must be jointly estimated with the target tracks. Multi-frame assignment PMHT (MF- PMHT) is an algorithm designed to mitigate some performance problems associated with PMHT. In MF-PMHT, the PMHT algorithm is applied to multi-frame sequences in the last L frames of data and considers the set of all possible measurement sequences. While effective in improving tracking performance compared to PMHT, performance of the original MF-PMHT degrades when the target single-frame detection probability is non-unity. This is because missed detections are not considered in the multi-frame sequences. A new MF-PMHT implementation is derived in this paper which explicitly considers missed detections in the multi-frame sequences. Performance of this MF-PMHT is compared to the original MF-PMHT algorithm as well as to a Homothetic PMHT. Simulation results indicate that the new MF- PMHT algorithm performs the same as the original algorithm when there are no missed detections and also performs better than the alternative algorithms considered when there are missed detections.

Keywords

clutter; expectation-maximisation algorithm; probability; sensor fusion; signal detection; target tracking; PMHT; clutter; data association; expectation-maximization algorithm; missed signal detection; multiframe assignment; multiframe sequence; multiple target tracking; probabilistic multihypothesis tracking; Algorithm design and analysis; Computational complexity; Computational modeling; Convergence; Degradation; Maximum likelihood detection; Maximum likelihood estimation; Milling machines; State estimation; Target tracking; MHT; Multi-hypothesis tracking; PMHT; data association; estimation; expectation-maximization; missed detections;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Fusion, 2007 10th International Conference on

Conference_Location

Quebec, Que.

Print_ISBN

978-0-662-45804-3

Electronic_ISBN

978-0-662-45804-3

Type

conf

DOI

10.1109/ICIF.2007.4408016

Filename

4408016