Gaussian mixture multiple-model multi-Bernoulli filters for nonlinear models via unscented transforms

The multiple-model multi-Bernoulli (MM-MB) filter is a new attractive approach for estimating multiple maneuvering targets in the presence of clutter, missed detection and data association uncertainty. In this paper, we extend the Gaussian Mixture (GM) MM-MB filter to nonlinear models by using unscented transform techniques. Moreover, in order to improve the robustness and numerical stability of the unscented Kalman (UK) GM-MM-MB filtering algorithm, we propose the square-root UK (SUK) GM implementation of the MM-MB filter for nonlinear models. A numerical example is presented to verify the effectiveness of the UK-GM-MM-MB and SUK-GM-MM-MB filtering approaches. Simulation results also show that the SUK-GM-MM-MB filtering approach produces the same filtering accuracy as the UK-GM-MM-MB filtering approach.