Multiple‐model Gaussian mixture probability hypothesis density filter based on jump Markov system with state‐dependent probabilities

The Gaussian mixture probability density (GM-PHD) filter has become a popular approach to solve the multiple-target tracking (MTT) problem because it can effectively and efficiently estimate number of targets target states that change over time from noisy measurements. In GM-PHD filter, detection survival probabilities, birth rate are assumed be constant, irrespective state. However, in some applications, for example, when follows planned trajectory, detection, depend on its Besides, reaches waypoint along will take manoeuvre go next waypoint, but does not accommodate manoeuvring switch between several motion models. To address this, we propose multiple-model with state-dependent which explicitly consider mode transition probabilities survival, as function performance proposed algorithm is demonstrated by illustrative MTT scenarios include manoeuvres during occlusion.