A sequential asynchronous multirate multisensor data fusion algorithm for state estimation

To generate the optimal state estimate, the fusion of information from multirate multisensors with asynchronous sampling rates is studied. The problem is formulated by linear time-vary dynamic system combined with multiple sensors observing a single target with asynchronous and different sampling rates. Unlike the traditional interpolation or batch process approaches, the state estimate is achieved by system prediction followed by sequential update along sensors. In updating, Kalman filter and the orthogonal projection theorem are used. The optimality of the algorithm in the sense of linear minimum variance is verified. Experimental results show the feasibility and the effectiveness of the presented approach.