A data fusion algorithm for multi-sensor dynamic system based on Interacting Multiple Model

We present an algorithm of data fusion estimation for dynamic system with multi-sensor and uncertain system models based on Kalman filtering and Interacting Multiple Model. The algorithm estimates the target state using interacting multiple model filtering method after using augmented multi-sensor fusion method. And this method is also available when the system contains different kinds of sensors or the measurement errors of different sensors are related. We test and verify the feasibility of this estimation algorithm through simulation and discuss the effect of the number of sensor on the estimation precision. Results show that, simply increasing the number of sensor cannot always improve the performance of the estimation. Type and number of sensor should be optimized in practical applications.