Sequential Inverse Covariance Intersection Fusion Steady-state Kalman Filter for Multi-Sensor Systems with Multiple Time-delayed Measurements

Abstract

In order to settle the fusion problem of multi-sensor systems with multiple time-delayed measurements, a steady-state suboptimal Kalman filter is derived, and the Inverse Covariance Intersection (ICI) fusion method is adopted which can be utilized in the system with unknown common information shared between sensors, and whose major advantage is that it is proved to be more accurate and represents an optimal-consistent and tight-solution to the problem of fusing estimates which share unknown common information. Then the Sequential ICI fusion steady-state Kalman filter is obtained. A simulation example shows the accuracy, the consistence and the tightness of the ICI fusing method, and the effectiveness of the presented fusion Kalman filter.