Abstract
This paper describes the joint probabilistic data association-feedback particle filter (JPDA-FPF) introduced in our earlier paper [1]. The JPDA-FPF is based on the feedback particle filter concept (see [2],[3]). A remarkable feature of the JPDA-FPF algorithm is its innovation error-based feedback structure, even with data association uncertainty in the general nonlinear case. The classical Kalman filter-based joint probabilistic data association filter (JPDAF) is shown to be a special case of the JPDA-FPF. A multiple target tracking application is presented: In the application, bearing only measurements with multiple sensors are used to track targets in the presence of data association uncertainty. It is shown that the algorithm is successfully able to track targets with significant uncertainty in initial estimate, and even in the presence of certain "track coalescence" scenarios.
Original language | English (US) |
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Title of host publication | 15th International Conference on Information Fusion, FUSION 2012 |
Pages | 2058-2064 |
Number of pages | 7 |
State | Published - 2012 |
Event | 15th International Conference on Information Fusion, FUSION 2012 - Singapore, Singapore Duration: Sep 7 2012 → Sep 12 2012 |
Other
Other | 15th International Conference on Information Fusion, FUSION 2012 |
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Country | Singapore |
City | Singapore |
Period | 9/7/12 → 9/12/12 |
ASJC Scopus subject areas
- Information Systems