Networked multi-sensor fusion estimation with delays, packet losses and missing measurements

This paper is concerned with the design of networked multi-sensor fusion estimation system (NMFES). The Kalman filtering problem is considered for the NMFES with random observation delays, packet dropouts and missing measurements caused by sensor failures. For each observation subsystem, the sensor failure phenomenon is described by a Bernoulli distributed white sequence with a known conditional probability, and the packet dropout phenomenon and randomly delayed measurements are described by multiple binary random variables. Without resorting to the augmentation technique, an optimal recursive fusion filter for NMFES is obtained in the linear minimum variance sense by using the innovation analysis method. The dimension of the designed filter is the same to the original system, which can help reduce computation costs as compared with the augmentation method. Moreover, the performance of the designed Kalman filter is dependent on the missing rates of the measurements, the upper bounds of random delays and the occurrence probabilities of delays. Finally, the effectiveness of the proposed results is demonstrated by an illustrative example.