Multiple Gaussian Processes based Extended Target Tracking with Variational Inference

In this work, the multiple GP-based filter (MGP) is proposed for tracking an extended target with a random shape. This model is able to represent one or a group of targets, where the shape of each sub-target is described by the GP model. Utilizing the MGP method for extended target tracking, the mainly challenge are as follows: (1) the data association problem of the inter-cluster; (2) the challenge of approximating nonconjugate distributions. In order to solve the association problem of the measurement with the sub-targets, a latent variable is defined to correlate them. Due to the lack of conjugacy, the resulting posterior is not possible to find a closed-form analytical expression. This paper uses variational inference to solve the problem and approximate the true posterior. The effectiveness of the proposed algorithm is evaluated through simulation.