Distributed Fusion Filter for Multi-sensor Descriptor Systems with Multiple Packet Dropouts Based on Prediction Compensation

Abstract

This paper is concerned with the distributed fusion filtering problem for descriptor systems with multiple sensors and multiple packet dropouts. When a packet is lost from a sensor to a local processor over an unreliable network, the predictor of the lost packet is used as a compensator. The original high-order descriptor system is transformed into two reduced-order subsystems (ROSs) by a non-singular linear transformation. By using projection theory, local filters (LFs) are presented for two ROSs in the linear unbiased minimum variance (LUMV) sense. The cross-covariance matrices between any two sensor subsystems and between two ROSs are derived to compute fusion weights, respectively. The distributed fusion filters (DFFs) weighted by matrices are presented for two ROSs in the LUMV sense. Then, DFF for the original descriptor system is obtained. A numerical example is used to show the effectiveness of the presented algorithm.