Object Tracking in Random Access Sensor Networks: Extended Kalman Filtering with State Overlapping

Alimadadi, Mohammadreza; Stojanovic, Milica; Closas, Pau

doi:10.1109/infcomw.2019.8845239

Cited by 3 publications

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“…Time series forecasting [ 16 ], distributed estimation in wireless sensor networks [ 17 , 18 , 19 ], optimal linear fusion for multi-dimensional cases [ 20 ], distributed fusion by adapting methods for graphical models [ 21 ], and different consensus, gossip, or diffusion algorithms [ 22 , 23 , 24 ] are some of the approaches proposed. Our previous work addressed distributed tracking in underwater acoustic sensor networks [ 25 , 26 ]. We designed a scalable method for large area coverage, in which multiple fusion centers, each overseeing sensors within a smaller footprint, exchange local tracking information to reach a consensus.…”

Section: Introductionmentioning

confidence: 99%

Delay-Tolerant Distributed Inference in Tracking Networks

Alimadadi

Stojanovic

Closas

2021

Sensors

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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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