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A parallel square-root algorithm for modified extended Kalman filter

机译:改进的扩展卡尔曼滤波器的并行平方根算法

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A parallel square-root algorithm and its systolic array implementation are proposed for performing modified extended Kalman filtering (MEKF). The proposed parallel square-root algorithm is designed based on the singular value decomposition (SVD) and the Faddeev algorithm, and a very large scale integration (VLSI) systolic array architecture is developed for its implementation. Compared to other square root Kalman filtering algorithms, the proposed method is more numerically stable. The VLSI architecture described has good parallel and pipelining characteristics in applying to the MEKF and achieves higher efficiency. For n-dimensional state vector estimations, the proposed architecture consists of O(2n/sup 2/) processing elements and uses O((s+17)n) time-steps for a complete iteration at each instant, in contrast to the complexity of O((s+6)n/sup 3/) time-steps for a sequential implementation, where s approximately=log n.
机译:提出了一种并行平方根算法及其脉动阵列实现,以执行改进的扩展卡尔曼滤波(MEKF)。基于奇异值分解(SVD)和Faddeev算法设计了并行平方根算法,并为实现实现开发了超大规模集成电路(VLSI)脉动阵列结构。与其他平方根卡尔曼滤波算法相比,该方法在数值上更稳定。所描述的VLSI架构在应用于MEKF时具有良好的并行和流水线特性,并实现了更高的效率。对于n维状态向量估计,所提出的体系结构由O(2n / sup 2 /)处理元素组成,并在每个时刻使用O((s + 17)n)个时间步进行完整迭代,这与复杂性形成对比顺序执行的O((s + 6)n / sup 3 /)个时间步长,其中s大约= log n。

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