Bayesian Filtering With Random Finite Set Observations

This paper presents a novel and mathematically rigorous Bayes recursion for tracking a target that generates multiple measurements with state dependent sensor field of view and clutter. Our Bayesian formulation is mathematically wellfounded due to our use of a consistent likelihood function derived from random finite set theory. It is established that under certain assumptions, the proposed Bayes recursion reduces to the cardinalized probability hypothesis density (CPHD) recursion for a single target. A particle implementation of the proposed recursion is given. Under linear Gaussian and constant sensor field of view assumptions, an exact closed form solution to the proposed recursion is derived, and efficient implementations are given. Extensions of the closed form recursion to accommodate mild non-linearities are also given using linearization and unscented transforms.

• Type: Journal
• Year: 2008
• Where: TSP
• Authors: Ba-Tuong Vo, Ba-Ngu Vo, Antonio Cantoni
• Keywords: Recursion, Rigorous Bayes Recursion, Sensor Field, TSP 2008