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|Title:||Multiple targets tracking by optimized particle filter based on multi-scan JPDA|
Unknown Process Noises
|Source:||Jing, L.,Vadakkepat, P. (2004). Multiple targets tracking by optimized particle filter based on multi-scan JPDA. Conference Record - IEEE Instrumentation and Measurement Technology Conference 1 : 303-308. ScholarBank@NUS Repository.|
|Abstract:||In this paper, the particle filter is used to solve the nonlinear and non-Gaussian estimation problem in multiple targets tracking and multiple sensor fusion process. The. weight of the particle is evaluated through the combination of Joint Probability Data Association (JPDA) and Multiple Hypothesis Tracking (MHT), which makes the probabilistic assignment based on all reasonable hypothesis in a sliding window of multiple scans. To track the multiple targets with random varying velocities, each particle's state is optimized based on the history information from the previous scans in the sliding window and group information in the current scan. The panicle diversity is enriched while the trajectory of each panicle evolve towards the high posterior density distribution. Moreover the problem of tracking newly appeared objects or disappeared objects are also discussed. The simulation results show that the improved particle filter method achieves dynamic stability and robustness while tracking multiple random moving targets.|
|Source Title:||Conference Record - IEEE Instrumentation and Measurement Technology Conference|
|Appears in Collections:||Staff Publications|
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