中文摘要：

多传感器信息融合理论大都是在无系统偏差和量测同步的理想条件下给出的,而实际的工程背景却并非如此。因此,在实际的工程实践中发现融合的效果并不理想,有的甚至不如融合前的效果好。为解决这一问题,研究了系统偏差条件下的多传感器异步融合问题。针对不同的工程背景,提出了两种异步融合算法：异步卡尔曼滤波（asynchronous Kalman filter,ASKF）算法和扩展异步卡尔曼滤波（generalized asynchronous Kalman fil-ter,GASKF）算法。仿真表明,两种算法实现了异步量测条件下目标状态和系统偏差的有效跟踪。

英文摘要：

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.