Event-Triggered Particle Filtering Via Diffusion Strategies for Distributed Estimation in Autonomous Systems
Somayeh Davar, Arash Mohammadi, Konstantinos N. Plataniotis
Abstract
The paper is motivated by recent advancements and developments in large, distributed, autonomous, and self-aware systems such as autonomous vehicles and vehicle-to-everything (V2X) technologies, where bandwidth, security, privacy, and/or power considerations limit the number of information transfers between neighbouring agents. In this regard, we propose an event-triggered distributed state estimation via diffusion strategies (ET/DPF), which is a systematic and intuitively pleasing distributed state estimation algorithm that jointly incorporates point and set-valued measurements within the particle filtering framework. In the absence of a measurement form a neighbouring node (i.e., having a set-valued measurement), each local agent/node evaluates the probability that the unknown measurement belongs to the event-triggering set based on its particles which is then used to update the corresponding particle weights. In our Monte Carlo simulations, the proposed ET/DPF outperforms its counterparts in environments with limited bandwidth or/and intermittent connectivity.
BibTeX
@inproceedings{icassp2018_eventtriggeredpa,
title = {Event-Triggered Particle Filtering Via Diffusion Strategies for Distributed Estimation in Autonomous Systems},
author = {Somayeh Davar and Arash Mohammadi and Konstantinos N. Plataniotis},
booktitle = {ICASSP 2018},
year = {2018}
}