MSc SS Thesis Presentation

Gaussian Process enhanced Distributed Particle filtering

Rui Tang

In many applications of multi-agent networks, the physical systems consist of massive nonlinear and non-Gaussian elements. Hence, in the first decade of this century, intensive research on distributed particle filters (DPFs) has been conducted to address the distributed estimation problems. For distributed algorithms, communication overhead is an important metric in terms of engineering feasibility. In previous work, the approach to distributed particle filtering relies on a parameterization of the posterior proba-bility or likelihood function, to reduce communication requirements. However, as more and more effective resampling algorithms are proposed, the dependence of particle filter performance on particle set size is greatly reduced, so this thesis attempts to explore the possi-bility of DPFs based on direct particle exchange. In this thesis, the Gaussian process enhanced resampling algorithm is used. Meanwhile, several metaheuristic optimization algorithms (i.e., genetic algorithm and firefly algorithm) are further adapted to seek the global optimal particle set to improve the estimation performance. Furthermore, all algorithms are simulated in target tracking scenarios and are evaluated from three aspects: time, space, and communication complexity.

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