Cardinalised probability hypothesis density tracking algorithm for extended objects with glint noise
Cuiyun Li, Rong Wang, Yu Hen Hu, Jinbin Wang · IET Science Measurement & Technology · 2016
The authors present a novel cardinalised probability hypothesis density (CPHD) algorithm under the assumption of a glint measurement noise model with unknown inverse covariance. Noise parameters are assumed to have a Gamma prior distribution so that the predicted and updated PHDs can have mixture of Gaussians representations. A variational Bayesian expectation maximisation procedure is applied to iteratively estimate parameters of the mixture distributions through random hypersurface model CPHD prediction and update steps. Simulation results show that the proposed algorithm can adaptively track extended objects with unknown object number and glint measurement noise, while achieving higher precision compared against the traditional approach.