Centralized multi-sensor multi-target data fusion with tracks as measurements

Emily Hunter; Daniel E. Clark; Bhashyam Balaji; Sean O'Rourke

doi:10.1117/12.2662334

14 June 2023 Centralized multi-sensor multi-target data fusion with tracks as measurements

Emily Hunter, Daniel E. Clark, Bhashyam Balaji, Sean O'Rourke

Proceedings Volume 12547, Signal Processing, Sensor/Information Fusion, and Target Recognition XXXII; 1254702 (2023) https://doi.org/10.1117/12.2662334
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Tracking systems often provide sets of tracks rather than raw detections obtained from sensors. Integrating these track sets into other tracking systems is challenging because the usual sensor models do not apply. In this work we present a method for fusing track data from multiple sensors in a central fusion node. The algorithm exploits the covariance intersection algorithm as a pseudo-Kalman filter which is integrated into a multi-sensor multi-target tracker within a Bayesian paradigm. This makes it possible to (i) integrate the proposed fusion method seamlessly into any existing tracker; (ii) modify multi-target trackers to take a set of tracks as a set of measurements; and (iii) perform gating to enable data association between tracks. The described method is demonstrated in simulations using several target trackers within the Stone Soup tracking framework.

Conference Presentation

Citation Download Citation

Emily Hunter, Daniel E. Clark, Bhashyam Balaji, and Sean O'Rourke "Centralized multi-sensor multi-target data fusion with tracks as measurements", Proc. SPIE 12547, Signal Processing, Sensor/Information Fusion, and Target Recognition XXXII, 1254702 (14 June 2023); https://doi.org/10.1117/12.2662334

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