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A Computationally Efficient Approach for Distributed Sensor Localization and Multitarget Tracking
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DOI：10.1109/LCOMM.2019.2954888.png)
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Abstract
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In the context of distributed target tracking based on a mobile peer-to-peer sensor network, the relative locations between the sensors are critical for their internode information exchange and fusion. For accurate coordinate calibration between the neighboring sensors, namely sensor localization, we propose a computationally efficient approach that minimizes the mismatch error between position estimates of the common targets yielded at neighbor sensors. This mismatch error is given by a Wasserstein-like distance that is a mean square error between two sets of position estimates which are associated efficiently via Hungarian assignment. Simulations have demonstrated that our approach, on the testbed of an arithmetic average fusion based probability hypothesis density filter, performs similar to the cutting-edge approach based on loopy belief propagation, but computes much faster and has much lower communication cost.
Keywords:
Nickel
Target tracking
Surveillance
Calibration
Computational modeling
Belief propagation
Clutter
Distributed fusion
target tracking
sensor registration
sensor localization
arithmetic average fusion
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INFORMATION FUSION
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