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Research on ellipsoidal intersection fusion method with unknown correlation

机译:相关性未知的椭球相交融合方法研究

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This paper reviews the advantages and shortages of the covariance intersection (CI) and ellipsoidal intersection (EI) methods for decentralized state fusion with unknown correlation, and makes some progress on both of them. New results are: a). For CI method, the convexity property is proved for the two classical cost functions (i.e., trace and natural logarithm of determinant of the fused covariance), and a simple form of the optimization conditions is derived for the latter one. Furthermore, a fast 2-sensor CI algorithm is proposed by expressing the cost function in scalar form. b). For the 2-sensor EI algorithm which minimized the natural logarithm of determinant of the mutual covariance, a new proof for its optimality is presented, which partly makes up the gap in [17]. Simulation results show the efficiency for both the 2-sensor CI and EI algorithms.
机译:本文回顾了协方差相交(CI)和椭圆相交(EI)方法在未知状态下的分散状态融合中的优缺点,并在两者上都取得了一些进展。新的结果是:a)。对于CI方法,证明了两个经典成本函数(即融合协方差的行列式的迹线和自然对数)的凸性,并为后者推导了优化条件的简单形式。此外,通过以标量形式表示成本函数,提出了一种快速的2-传感器CI算法。 b)。对于最小化相互协方差行列式的自然对数的2传感器EI算法,提出了一种关于其最优性的新证明,这部分弥补了[17]中的空白。仿真结果表明了两传感器CI和EI算法的效率。

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