Collaborative State Fusion in Partially Known Multi-agent Environments

TL;DR

This paper introduces LoF, a novel two-stage collaborative fusion framework for multi-agent target tracking that effectively handles model mismatches and sensor outliers, improving estimation accuracy.

Contribution

The paper proposes LoF, a learnable weighted robust fusion method combining local estimators and outlier-resistant schemes for better multi-agent target state estimation.

Findings

LoF achieves 9.1% higher fusion gain than existing methods.

LoF effectively handles model mismatch and sensor outliers.

Experimental results validate the robustness and accuracy of LoF.

Abstract

In this paper, we study the collaborative state fusion problem in a multi-agent environment, where mobile agents collaborate to track movable targets. Due to the limited sensing range and potential errors of on-board sensors, it is necessary to aggregate individual observations to provide target state fusion for better target state estimation. Existing schemes do not perform well due to (1) impractical assumption of the fully known prior target state-space model and (2) observation outliers from individual sensors. To address the issues, we propose a two-stage collaborative fusion framework, namely \underline{L}earnable Weighted R\underline{o}bust \underline{F}usion (\textsf{LoF}). \textsf{LoF} combines a local state estimator (e.g., Kalman Filter) with a learnable weight generator to address the mismatch between the prior state-space model and underlying patterns of moving targets. Moreover, given observation outliers, we develop a time-series soft medoid(TSM) scheme to perform robust fusion. We evaluate \textsf{LoF} in a collaborative detection simulation environment with promising results. In an example setting with 4 agents and 2 targets, \textsf{LoF} leads to a 9.1\% higher fusion gain compared to the state-of-the-art.