Fusion of Sensor Measurements and Target-Provided Information in Multitarget Tracking

TL;DR

This paper introduces a probabilistic framework and scalable algorithm for multitarget tracking that fuses heterogeneous sensor data from perception systems and reporting targets, handling uncertainties like missed detections and false alarms.

Contribution

It develops a joint observation model and factor graph-based algorithm that effectively combines data from independent sensors and cooperative targets for improved tracking accuracy.

Findings

The algorithm performs well on simulated data.

It successfully tracks targets in maritime surveillance data.

Fusion of heterogeneous data improves tracking robustness.

Abstract

Tracking multiple time-varying states based on heterogeneous observations is a key problem in many applications. Here, we develop a statistical model and algorithm for tracking an unknown number of targets based on the probabilistic fusion of observations from two classes of data sources. The first class, referred to as target-independent perception systems (TIPSs), consists of sensors that periodically produce noisy measurements of targets without requiring target cooperation. The second class, referred to as target-dependent reporting systems (TDRSs), relies on cooperative targets that report noisy measurements of their state and their identity. We present a joint TIPS-TDRS observation model that accounts for observation-origin uncertainty, missed detections, false alarms, and asynchronicity. We then establish a factor graph that represents this observation model along with a state evolution model including target identities. Finally, by executing the sum-product algorithm on that factor graph, we obtain a scalable multitarget tracking algorithm with inherent TIPS-TDRS fusion. The performance of the proposed algorithm is evaluated using simulated data as well as real data from a maritime surveillance experiment.