Learning Recursive Bayesian Nonparametric Modeling of Moving Targets via Mobile Decentralized Sensors

Bayesian nonparametric models, such as the Dirichlet Process Gaussian Process (DPGP), have been shown very effective at learning models of dynamic targets exclusively from data. Previous work on batch DPGP learning and inference, however, ceases to be efficient in multi-sensor applications that require decentralized measurements to be obtained sequentially over time. Batch processing, in this case, leads to redundant computations that may hinder online applicability. This paper develops a recursive approach for DPGP learning and inference in which a novel Dirichlet Process prior based on Wasserstein metric is used for measuring the similarity between multiple Gaussian Processes (GPs). Combined with the GP recursive fusion law, the proposed recursive DPGP fusion approach enables efficient online data fusion. The problem of active sensing for recursive DPGP learning and inference is also investigated by uncertainty reduction via expected mutual information. Simulation and experimental results show that the proposed approach successfully learns the models of moving targets and outperforms existing benchmark methods.