Abstract
The probability hypothesis density (PHD) filter and cardinalized probability hypothesis density (CPHD) filter are principled approximations of the general multitarget Bayes recursive filter. If n is the current number of tracks and m the current number of measurements, then the former has computational complexity O(mn) and the latter O(m3 n). Although the CPHD filter has better target detection and tracking performance than the PHD filter, its cubic complexity in m will limit its practicality in many applications. The primary purpose of this paper is to derive a CPHD filter that, because of a simplified clutter model, has computational complexity O(mn). I also show how to extend this new CPHD filter to the multisensor case.
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