An event based multi-sensor fusion algorithm with deadzone like measurements

•An event based multi-sensor fusion algorithm with deadzone like measurements is proposed.•A modified Kalman filter for deadzone like measurements is derived.•The information form of modified Kalman filter is also given.•Existing Tobit Kalman filter and Kalman filter are special cases of our results...

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Vydáno v:Information fusion Ročník 42; s. 111 - 118
Hlavní autoři: Wang, Guoqing, Li, Ning, Zhang, Yonggang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.07.2018
Témata:
Deadzone
Event based
Information fusion
Kalman filter
Sensor networks
Sensor networks
Deadzone
Event based
Kalman filter
Information fusion
ISSN:1566-2535, 1872-6305
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Shrnutí:•An event based multi-sensor fusion algorithm with deadzone like measurements is proposed.•A modified Kalman filter for deadzone like measurements is derived.•The information form of modified Kalman filter is also given.•Existing Tobit Kalman filter and Kalman filter are special cases of our results.•Experiments demonstrate the advantages of our proposed algorithms. Event based strategy has received increasing attention recent years since it can provide a useful compromise between estimation performance and constrained energy or communication resources. In this paper, we propose an event based multi-sensor fusion algorithm with deadzone like measurements, where every sensor compares its measurements with an interval, and only elements beyond the thresholds, i.e., outside the deadzone, are sent to the fusion centre. To fuse this kind of deadzone like measurement, we firstly derive a modified Kalman filter (KF), which is based on the statistical properties of measurements. Then we obtain its information form, which is utilised in our event based information fusion algorithm to further release the computation burden caused by multi-sensor. Existing standard Tobit KF and KF are special cases of our modified KF, and simulation results demonstrate the advantages of the proposed event based algorithm as compared with several existing methods.
ISSN:1566-2535
1872-6305
DOI:10.1016/j.inffus.2017.10.004