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

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.