Cooperative Localization and Multitarget Tracking in Agent Networks with the Sum-Product Algorithm

TL;DR

This paper presents a scalable, graph-based sum-product algorithm for joint cooperative localization and multitarget tracking in agent networks, effectively handling clutter and miss detection, with demonstrated superiority over separate methods.

Contribution

It introduces a unified, message passing approach that combines localization and tracking, applicable to multistatic networks, improving performance by leveraging shared information.

Findings

Superior performance over separate localization and tracking methods.

Effective in multistatic network configurations with multiple transmitters and receivers.

Validated with real-world data from autonomous underwater vehicles.

Abstract

This paper addresses the problem of multitarget tracking using a network of sensing agents with unknown positions. Agents have to both localize themselves in the sensor network and, at the same time, perform multitarget tracking in the presence of clutter and miss detection. These two problems are jointly resolved using a holistic and centralized approach where graph theory is used to describe the statistical relationships among agent states, target states, and observations. A scalable message passing scheme, based on the sum-product algorithm, enables to efficiently approximate the marginal posterior distributions of both agent and target states. The proposed method is general enough to accommodate a full multistatic network configuration, with multiple transmitters and receivers. Numerical simulations show superior performance of the proposed joint approach with respect to the case in which cooperative self-localization and multitarget tracking are performed separately, as the former manages to extract valuable information from targets. Lastly, data acquired in 2018 by the NATO Science and Technology Organization (STO) Centre for Maritime Research and Experimentation (CMRE) through a network of autonomous underwater vehicles demonstrates the effectiveness of the approach in a practical application.