Multi-Rate Data Fusion for Wireless Sensor Networks with Time-Delay Based on Improved Cubature Kalman Filter

Aiming at the problem of time-delay in wireless sensor networks (WSNs), a multi-rate fusion method is proposed from the perspective of data fusion. The sensor uses different sampling and transmission rates for data collection and transmission. Firstly, in order to enhance the adaptability of cubature Kalman filter (CKF), the filtering performance of CKF is further improved. In this paper, the covariance matrix is modified online by constructing adaptive factors. The improved cubature Kalman filter (ICKF) is used to process these data collected by sensor nodes to generate local estimation (LE), and then the LE is formed into fusion estimation (FE) in the fusion center. The simulation results show that, compared with the traditional CKF and unscented Kalman filter (UKF), the ICKF algorithm has higher estimation performance and better stability.