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, 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 ...
Beard, Michael; Vo, Ba; Vo, Ba-Ngu; Arulampalam, S. (2013)The conventional GMPHD/CPHD filters require the PHD for target births to be a Gaussian mixture (GM), which is potentially inefficient because careful selection of the mixture parameters may be required to ensure good ...
Mahler, R.; Vo, Ba Tuong; Vo, Ba-Ngu (2011)In Bayesian multi-target filtering, we have to contend with two notable sources of uncertainty, clutter and detection. Knowledge of parameters such as clutter rate and detection profile are of critical importance in ...