Efficient multisensor fusion with sliding window Kalman filtering for discrete-time uncertain systems with delays

In this study, we provide two computationally effective multisensory fusion filtering algorithms for discrete-time linear uncertain systems with state and observation time-delays. The first algorithm is shaped by algebraic forms for multirate sensor systems, and then we propose a matrix form of filtering equations using block matrices. The second algorithm is based on exact cross-covariance matrix equations. These equations are useful to compute matrix weights for fusion estimation in a multidimensional-multisensor environment. Furthermore, our proposed filtering algorithms are based on the sliding window strategy in order to achieve high estimation accuracy and stability under parametric uncertainties. The authors demonstrate the low computational complexities of the proposed fusion filtering algorithms and how the proposed algorithms robust against dynamic model uncertainties comparing with Kalman filtering with time delays.