Adaptive Target Birth Intensity for PHD and CPHD Filters
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The standard formulation of the probability hypothesis density (PHD) and cardinalised PHD (CPHD) filters assumes that the target birth intensity is known a priori. In situations where the targets can appear anywhere in the surveillance volume this is clearly inefficient, since the target birth intensity needs to cover the entire state space. This paper presents a new extension of the PHD and CPHD filters, which distinguishes between the persistent and the newborn targets. This extension enables us to adaptively design the target birth intensity at each scan using the received measurements. Sequential Monte-Carlo (SMC) implementations of the resulting PHD and CPHD filters are presented and their performance studied numerically. The proposed measurement-driven birth intensity improves the estimation accuracy of both the number of targets and their spatial distribution.
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Mahler, Ronald (2014)The "background-agnostic" CPHD filter was introduced at the 2010 SPIE Defense, Security and Sensing Symposium in 2010. It is a CPHD filter that is capable of operation when both the clutter background and the target-detection ...
Mahler, Ronald (2012)This paper describes a general approach for deriving PHD/CPHD filters that must estimate the background clutter process, rather than being provided with it a priori. I first derive general time- and measurementupdate ...
Mahler, R.; Vo, Ba Tuong (2014)The “clutter-agnostic” CPHD filter was introduced at the 2010 SPIE Defense, Security and Sensing Symposium in 2010, and has been investigated in subsequent papers. This “k-CPHD filter” was capable of multitarget detection ...