Data-Driven Distributed State Estimation and Behavior Modeling in Sensor Networks

TL;DR

This paper introduces a novel approach for simultaneous distributed state estimation and behavior learning in sensor networks, extending Gaussian process-based Bayes filters to an online, distributed framework, validated on synthetic and real robot data.

Contribution

It is the first to formalize and address the combined problem of state estimation and behavior learning in sensor networks using an extended GP-BayesFilters approach.

Findings

Effective in tracking objects with unknown behaviors

Validated on synthetic and multi-robot data

Demonstrates improved accuracy in distributed settings

Abstract

Nowadays, the prevalence of sensor networks has enabled tracking of the states of dynamic objects for a wide spectrum of applications from autonomous driving to environmental monitoring and urban planning. However, tracking real-world objects often faces two key challenges: First, due to the limitation of individual sensors, state estimation needs to be solved in a collaborative and distributed manner. Second, the objects' movement behavior is unknown, and needs to be learned using sensor observations. In this work, for the first time, we formally formulate the problem of simultaneous state estimation and behavior learning in a sensor network. We then propose a simple yet effective solution to this new problem by extending the Gaussian process-based Bayes filters (GP-BayesFilters) to an online, distributed setting. The effectiveness of the proposed method is evaluated on tracking objects with unknown movement behaviors using both synthetic data and data collected from a multi-robot platform.