Event-based Particle Filtering with Point and Set-valued Measurements

The paper is motivated by recent and rapid growth of Cyber-Physical Systems (CPS) and the critical necessity for preserving restricted communication resources in their application domains. In this context, a distributed state estimation architecture is considered where a remote sensor communicates its measurements to the fusion centre (FC) in an event-based fashion. We propose a systematic and intuitively pleasing distributed state estimation algorithm which jointly incorporates point and set-valued measurements within the particle filtering framework. Referred to as the event-based particle filter (EBPF), point-valued measurements are incorporated in the estimation recursion via a conventional particle filter formulation, while set-valued measurements are incorporated by developing an observation update step similar in nature to quantized particle filtering approach. More specifically, in the absence of an observation (i.e., having a set-valued measurement), the proposed EBPF evaluates the probability that the unknown observation belongs to the event-triggering set based on its particles which is then used to update the corresponding particle weights. The simulation results show that the proposed EBPF outperforms its counterparts specifically in low communication rates, and confirms the effectiveness of the proposed hybrid estimation algorithm.