Distributed Localization and Tracking of Mobile Networks Including Noncooperative Objects - Extended Version

TL;DR

This paper introduces a Bayesian distributed method for localizing and tracking both cooperative and noncooperative objects in mobile networks, combining particle belief propagation with consensus schemes for high accuracy and scalability.

Contribution

It presents a novel integrated approach that unifies cooperative self-localization and distributed tracking using probabilistic information transfer and low-complexity algorithms.

Findings

Significant improvements in localization accuracy over separate methods.

Effective scalability with increasing number of agents and objects.

Demonstrated robustness and efficiency through simulations.

Abstract

We propose a Bayesian method for distributed sequential localization of mobile networks composed of both cooperative agents and noncooperative objects. Our method provides a consistent combination of cooperative self-localization (CS) and distributed tracking (DT). Multiple mobile agents and objects are localized and tracked using measurements between agents and objects and between agents. For a distributed operation and low complexity, we combine particle-based belief propagation with a consensus or gossip scheme. High localization accuracy is achieved through a probabilistic information transfer between the CS and DT parts of the underlying factor graph. Simulation results demonstrate significant improvements in both agent self-localization and object localization performance compared to separate CS and DT, and very good scaling properties with respect to the numbers of agents and objects.