Chandrasekhar recursion for structured time-varying systems and its application to recursive least squares problems

P. Park, Y. Cho, T. Kailath
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引用次数: 6

Abstract

Chandrasekhar recursion of Kalman filtering for a time-varying system has not been fully studied while its counterpart for a time-invariant system has been around for decades. Sayed and Kailath (1992) have shown that Chandrasekhar recursion for a certain class of structured time-varying systems can be achieved. In this paper, the authors extend the traditional discrete-time Chandrasekhar recursion of Kalman filtering to derive an algorithm applicable to an even wider class of structured time-varying systems including those with so-called quasi-internally-invariant property. This extension makes it possible to update the Kalman filter of time-varying systems with a quasi-internally-invariant property, only with O(n(p+q)) flops instead of O(n/sup 3/), where n, p and q are the number of states, the number of outputs and the displacement rank of Riccati solutions, respectively. It is also shown that the resulting algorithm can be applied to adaptive filtering (specifically, recursive least squares problems).<>
结构化时变系统的Chandrasekhar递归及其在递归最小二乘问题中的应用
时变系统的钱德拉塞卡递归卡尔曼滤波尚未得到充分的研究,而定常系统的钱德拉塞卡递归卡尔曼滤波已经存在了几十年。Sayed和Kailath(1992)已经证明,对于某一类结构化时变系统可以实现钱德拉塞卡递归。本文对卡尔曼滤波的传统离散时间钱德拉塞卡递推进行了推广,导出了一种适用于更广泛的结构时变系统的算法,包括那些具有拟内不变性质的系统。这个扩展使得有可能更新时变系统的卡尔曼滤波器具有准内不变的性质,只有O(n(p+q))个flops而不是O(n/sup 3/),其中n, p和q分别是状态的数量,输出的数量和Riccati解的位移秩。结果还表明,所得算法可以应用于自适应滤波(特别是递归最小二乘问题)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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