Cooperative Filtering with Range Measurements: A Distributed Constrained Zonotopic Method

TL;DR

This paper introduces a distributed constrained zonotopic algorithm for multi-agent systems that improves state estimation accuracy using a combination of high- and low-accuracy range measurements, with low computational complexity.

Contribution

A novel distributed estimation method based on extended constrained zonotopes that enhances accuracy and efficiency in multi-agent systems with mixed measurement qualities.

Findings

The proposed algorithm effectively improves estimation accuracy.

Simulation results confirm the method's efficiency and accuracy.

The approach handles both high- and low-accuracy measurements in a distributed manner.

Abstract

This article studies the distributed estimation problem of a multi-agent system with bounded absolute and relative range measurements. Parts of the agents are with high-accuracy absolute measurements, which are considered as anchors; the other agents utilize lowaccuracy absolute and relative range measurements, each derives an uncertain range that contains its true state in a distributed manner. Different from previous studies, we design a distributed algorithm to handle the range measurements based on extended constrained zonotopes, which has low computational complexity and high precision. With our proposed algorithm, agents can derive their uncertain range sequentially along the chain topology, such that agents with low-accuracy sensors can benefit from the high-accuracy absolute measurements of anchors and improve the estimation performance. Simulation results corroborate the effectiveness of our proposed algorithm and verify our method can significantly improve the estimation accuracy.