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F. Meyer, P. Braca, F. Hlawatsch, M. Micheli, K. LePage:
"Scalable Adaptive Multitarget Tracking Using Multiple Sensors";
Poster: IEEE Globecom Workshops (GC Wkshps 2016), Washington D.C., USA; 12-04-2016 - 12-08-2016; in: "GLOBECOM-2016", (2016), 6 pages.

In networked mobile multitarget tracking systems, parameters such as detection probabilities, clutter rates, and motion model parameters are often unknown and time-varying. Such parameter variability can seriously degrade the performance of a multitarget tracking system. Here, we propose a Bayesian tracking framework in which the multisensor-multitarget tracking problem is formulated according to the measurement origin uncertainty paradigm and the unknown parameters-in the present case, the detection probabilities at the individual sensors-are modeled as Markov chains. The resulting Bayesian estimation problem is then solved using the belief propagation scheme. This approach results in a multisensor-multitarget tracking method that is able to adapt to the time variations of the detection probabilities. Moreover, the method has a low complexity that scales very well in all relevant system parameters. The performance of the method is assessed using data collected by a mobile underwater wireless sensor network.

Multitarget tracking, data association, belief propagation, message passing, factor graph, adaptive algorithm, underwater sensor network.

http://publik.tuwien.ac.at/files/publik_255598.pdf