Abstract:

Theory of multisensor data fusion is always proposed under the condition that there are no system errors and the measurements of different sensors are synchronous, which is not the true in the real engineering environment. Therefore, the effect of fusion is not always ideal in the real environment, sometimes it is even worse. To solve this problem, multisensor asynchronous data fusion under the condition influenced by system error is considered. For two different environments, two asynchronous data fusion algorithms, asynchronous Kalman filter (ASKF) algorithm and generalized asynchronous Kalman filter (GASKF) algorithm are proposed. Simulations show that these two algorithms track the states and system errors with asynchronous measurements of different sensors, effectively.