Event-Triggered Sequential Fusion Filter for Nonlinear Multi-Sensor Systems With Correlated Noise Based on Observation Noise Estimation
Guorui Cheng, Meng-chen Ma, Liguo Tan, Shenmin Song
Abstract
In this paper, a sequential fusion filter estimator based on event-triggered (ET) multi-sensor nonlinear systems and Cubature Kalman filter, that is ETCKF-SF, is studied, in which the observation noise of each sensor is synchronously correlated, and the observation noise of each sensor is correlated with the process noise at the previous moment. Because the energy of each sensor in the network system is limited, in order to reduce unnecessary sensor energy loss, an ET mechanism is used to reduce the communication rate from the sensor to the fusion center. Sequential fusion filter estimator based on ET mechanism multi-sensor nonlinear systems and innovation analysis is studied, firstly. At the same time, in the design process of the sequential fusion filter, the estimator of the observation noise based on the ET mechanism will also be designed. Secondly, the numerical implementation of the proposed algorithm will be derived based on the third-degree spherical-radial rule Cubature Kalman filter (CKF). Finally, the proposed algorithm is compared with the extended Kalman filter (EKF) based on Taylor series expansion. The effectiveness of the proposed algorithm is verified by the comparison of simulation examples.