Multi-sensor optimal information fusion Kalman filters with applications

Abstract

Using Kalman filtering theory, a new multi-sensor optimal information fusion algorithm weighted by matrices is presented in the linear minimum variance sense, which is equivalent to the maximum likelihood fusion algorithm under the assumption of normal distributions. The algorithm considers the correlation among local estimation errors, and it involves the inverse of certain matrix with high dimension. Another two new multi-sensor suboptimal information fusion algorithms weighted by vectors and weighted by scalars are given for reducing the computational burden and increasing the real-time property. Based on these fusion algorithms, the multi-sensor optimal and suboptimal information fusion Kalman filters with two-layer fusion structures are given. The simulation researches of the comparisons among them as well as the centralized filter in a radar tracking system with three sensors show their effectiveness.