可删除无序量测的交互多模型算法

Interacting Multiple Model Algorithm with Removing out-of-sequence Measurement

在集中式多传感器系统中,常会出现如何从当前状态估计中删去已使用过的旧量测的问题。Bar-shalom将需要被删除的量测也称为无序量测。Bar-shalom的可删除无序量测的交互多模型算法是基于标准交互多模型算法和等价量测提出的。然而,在标准交互多模型算法中正态概率密度函数和概率质量函数的混用,导致模型更新权值仅仅是一个近似概率值。并且,等价量测的使用会导致该算法有时候出现性能恶化和Rank Deficiency问题。针对这些缺点,基于最优多传感器融合规则和前向预测最优无序量测删除算法,提出了新型的可删除无序量测的交互多模型算法。该算法没有上述那些缺点,而且仿真表明了新算法的可行性和优越性。

In the centralized multi-sensor system,there is often a problem that how to remove measurements used early from the current state estimation,then re-calculate the current state estimation.The bar-shalom also named the measurement that needs to be removed as out-of-sequence measurement（OOSM）.The interacting multiple model（IMM） algorithm with removing OOSM presented by Bar-shalom is based on the standard IMM algorithm and Bl1 algorithm.However,in the standard IMM algorithm,the combination of normal probability density function and probability mass function results in that updated model weights are only an approximate probability.Moreover,sometimes the use of equivalent measurement can cause the performance of algorithm to deteriorate and the problem of Rank Deficiency.In order to deal with these drawbacks,based on the optimal multi-sensor fusion rule by Deng Sun and the globally optimal forward-prediction filtering algorithm with removing OOSM,a novel IMM algorithm is presented.The novel algorithm has not those drawbacks mentioned above.Simulation shows the feasibility and superiority of the new algorithm.