An ellipse extended target CBMeMBer filter using gamma and box-particle implementation

Abstract

This work presents a gamma box-particle implementation of the ellipse extended target cardinality balanced multi-target multi-Bernoulli (ET-CBMeMBer) filter, named the ellipse extended target gamma box particle CBMeMBer (EET-GBP-CBMeMBer) filter, where the ellipse extended targets are characterized using a Poisson model suggested by Gilholm et al. The proposed filter is capable of dynamically tracking multiple ellipse extended targets and estimating the target kinematics and extents as well as the measurement rates of targets, in the presence of clutter measurements, false alarms and missed detections. To get the CBMeMBer recursion of the EET-GBP-CBMeMBer filter, a suitable measurement likelihood is derived for a given partitioning cell, and the main gamma box-particle implementation are given along with the necessary box manipulations and approximations. The capabilities and limitations of the proposed EET-GBP-CBMeMBer filter are demonstrated by simulation examples. The simulation results show that a gamma box-particle implementation of the ET-CBMeMBer filter can avoid the high number of particles and reduce computational burden, compared to a particle implementation of that for extended target tracking.