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Scalable adaptive multitarget tracking using multiple sensors

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Abstract
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 multisensormultitarget 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.

URI
http://hdl.handle.net/20.500.12489/807

Report Number
CMRE-PR-2019-067

Source
In: 2016 IEEE Globecom Workshops, 4-8 December 2016, Washington DC, USA, doi: 10.1109/GLOCOMW.2016.7849034

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Date
2019/06

Author(s)
Meyer, Florian
; 
Braca, Paolo
; 
Hlawatsch, Franz
; 
Micheli, Michele
; 
LePage, Kevin D.

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