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

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.

Message Passing-Based 9-D Cooperative Localization and Navigation With Embedded Particle Flow

“…Possible future work is to extent the measurement model beyond Gaussian noise, like missed detections, clutter/false alarm measurements, and data association uncertainty of measurements [31], [33], [46], [47], or to cooperative radio signalbased SLAM algorithm with highly informative measurement models [28], [29], [56].…”

“…The sum-product rule is used to compute approximations ("beliefs") of the marginal posterior probability density functions (PDFs) of agent positions [18], [20]. BP-based methods are very flexible and have been successfully applied to many diverse applications as for example radio signal-based simultaneous localization and mapping (SLAM) [27]- [29], multiobject tracking [30]- [32], and cooperative multiobject tracking [33]. Their excellent scalability and distributed nature make BP-based algorithms a powerful tool for CL on large-scale networks [18]- [20].…”

“…In this work, we do not consider uncertainties beyond Gaussian noise, like missed detections, clutter/false alarm measurements, and data association uncertainty of measurements [31], [33], [46], [47]. This paper focuses on dynamic networks.…”

Monitoring of Underwater Critical Infrastructures: the Nord Stream and Other Recent Case Studies

“…Lat itud e [de gre es] length. On the other side, AUVs are untethered and computercontrolled, with little or no operator interaction while performing their pre-programmed subsea mission; multiple AUVs can also cooperatively form an intelligent sensing network for the monitoring of large regions of interest [35], [36]. Even though AUVs can be programmed to survey larger areas than ROVs, their operating range is dependent on the duration of their batteries.…”

3D Localization for Multiplatform Radar Networks with Deployable Nodes

“…Modern surveillance systems encompass the use of multiple cooperative, autonomous, and unmanned vehicles (see, e.g., [1]- [6]) in all the different operational domains, that is, air, land and sea [7], [8]. In this context a multi-platform radar network (MPRN) is envisioned as a next-generation sensing system capitalizing on several spatially separated transmitting, receiving and/or transmitting-receiving deployable sensing units.…”