Skip to Main Content
This paper is concerned with the optimal dynamic information fusion problem for asynchronous multirate multisensors. By establishing the state space models at each scale, a novel algorithm is developed to recursively fuse the data from multisensors, where the ratio between the sampling rates of different sensors is allowed to be any positive integer. Without using the traditional interpolation or augmentation approaches for states or measurements, the state estimate is obtained based on global measurements, and the obtained state estimate is then proven to be the optimal in the sense of linear minimum variance. It is shown that our main results improve and extend the existing information fusion algorithms for which the sampling rate ratio of different sensors is restricted to one or a power of two. Finally, the feasibility and efficiency of the proposed algorithm is illustrated by a numerical simulation example.