Generalised covariance intersection-Gamma Gaussian Inverse Wishart-Poisson multi-Bernoulli Mixture: An intelligent multiple extended target tracking scheme for mobile aquaculture sensor networks

Abstract

Poisson multi-Bernoulli Mixture (PMBM) filter has been known as an available or practical point and multiple extended target tracking (METT) method. The authors present an improved PMBM filter with adaptive detection probability and adaptive newborn distributions, accompanying with an associated distributed fusion strategy for the tracking extended multiple targets. First, the augmented state of unknown and changing target detection probability is assumed as Gamma (GAM) distribution. Second, extended states are described by Inverse Wishart (IW) distribution based on this augmented state, accompanying with dynamic states presented by Gaussian distribution. And then, an adaptive newborn distribution is adopted to describe the newborn targets appearing arbitrarily. Consequently, the closed-form solutions of the proposed filter can be derived by approximating the intensity of newborn and potential targets to the Gamma Gaussian Inverse Wishart (GGIW) form. Moreover, the fused means that Generalised Covariance Intersection (GCI) is performed in such a large-scale aquaculture sensor network. Experiments are presented to verify the availability of the GCI-GGIW-PMBM method, and comparisons with other METT filters also demonstrate that tracking behaviours are improved largely.