Adaptive Target Birth Intensity for PHD and CPHD Filters

By B. Ristic, Daniel E. Clark, B. Vo. IEEE Transactions on Aerospace and Electronic Systems, 2012

303 citations recorded by the source corpus.

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.

Subfields: Aerospace Engineering, Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/73e3044a23b6e27580756bf81b9b933011ba7f6d

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Citations in the corpus

A Tutorial on Bernoulli Filters: Theory, Implementation and Applications (2013, background): “four classes of its simplifications or principled approximations have emerged recently: the Bernoulli filter (also known as JoTT or joint target detection and tracking filter) [10], [15]– [18], the probability hypothesis density (PHD) filter [19]–[22], the cardinalised PHD filter [23], [24] and the ”