We consider a network of distributed underwater sensors whose task is to monitor the movement of objects across an area. The sensors measure the strength of signals emanated by the objects and convey the measurements to the local fusion centers. Multiple fusion centers are deployed to cover an arbitrarily large area. The fusion centers communicate with each other to achieve consensus on the estimated locations of the moving objects. We introduce two efficient methods for data fusion of distributed partial estimates when delay in communication is not negligible. We concentrate on the minimum mean squared error (MMSE) global estimator, and evaluate the performance of these fusion methods in the context of multiple-object tracking via extended Kalman filtering. Numerical results show the superior performance compared to the case when delay is ignored.
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Delay-Tolerant Distributed Inference in Tracking Networks
This paper discusses asynchronous distributed inference in object tracking. Unlike many studies, which assume that the delay in communication between partial estimators and the central station is negligible, our study focuses on the problem of asynchronous distributed inference in the presence of delays. We introduce an efficient data fusion method for combining the distributed estimates, where delay in communications is not negligible. To overcome the delay, predictions are made for the state of the system based on the most current available information from partial estimators. Simulation results show the efficacy of the methods proposed.
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- NSF-PAR ID:
- 10316503
- Editor(s):
- Paolo Spagnolo
- Date Published:
- Journal Name:
- IEEE sensors journal
- ISSN:
- 1558-1748
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/s21175747
