Data fusion and bias registration based on sensor selection for large-scale sensor networks
Junjun Guo, Chongzhao Han, Longfei Li · 2017
This paper presents a new target tracking and the possible changing bias registration approach based on sensor selection for large-scale distributed sensor networks. We try to address this target tracking problem at the following three steps. Firstly, local-level tracking is addressed based on the estimated sensor biases for each sensor, and only the state estimates are transmitted to the fusion center; Secondly, data fusion is carried out by using the sensor selection approach at the fusion center, target state is estimated based on the tracking results reported by the selected sensors; Finally, sensors' biases are updated at the fusion center. In addition, both of sensor coverage problem and the possible changing bias problem are considered in our paper. The proposed approach only needs to select a small number of sensors for tracking, rather than the traditional approaches, which prefer to use all of the sensors. Simulation results show the effectiveness of the proposed approach.