Event-Triggered Distributed Target Tracking via PRIMEX

TL;DR

This paper introduces event-triggered distributed target tracking algorithms based on PRIMEX, which improve communication efficiency while maintaining accuracy in multi-agent systems.

Contribution

The paper develops two PRIMEX-based distributed tracking algorithms with event-triggered mechanisms, enhancing communication efficiency in target tracking tasks.

Findings

PRIMEX-based methods achieve comparable tracking accuracy to centralized methods.

Event-triggered mechanisms reduce communication load significantly.

Algorithms outperform covariance intersection in distributed tracking scenarios.

Abstract

PRIMEX (prime-based graph encoding and extraction) is a recently proposed framework for scalable distributed fusion. In PRIMEX, the information pedigree of state estimates or probability density functions is encoded using the information codes, enabling lightweight arithmetic for redundancy removal and data integration. Building on PRIMEX and its memoryless fusion strategy based on a least-squares approximation, in this paper we present two efficient distributed tracking algorithms: a consensus-based PRIMEX method that fuses information from all neighbors, and a greedy gossip-based PRIMEX method that fuses with the most informative neighbor. To further increase communication efficiency, we incorporate an event-triggered mechanism, in which transmission decisions are driven by information novelty measured using differences between the information codes. The proposed methods are evaluated and compared with covariance intersection and centralized fusion in a distributed single target tracking scenario. Simulation results show that PRIMEX-based methods remain competitive in tracking accuracy while improving communication efficiency.