Abstract
We present a particle filter (PF)-based algorithm to detect and track
maneuvering infrared weak multiple targets at different signal-to-noise ratios for
the scenes with the multiple targets number unknown and varying. A detecting filter
and a tracking filter based on sequential likelihood ratio (LR) testing with fixed
sample size are designed, respectively, for capturing new target and tracking
confirmed targets. The algorithm is optimized with selectively particles sampling
and adaptive process noise. Targets birth and death time are accurately estimated
according to the change degree of the LR along with the corresponding state amended
through PF backward recursion. Simulation results show that it is positive to detect
and track maneuvering infrared weak multiple targets with the appearance and
disappearance of more than one, which also achieves a significant improvement in
state estimation especially for the time targets which appear and disappear.
© 2014 Chinese Optics Letters
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