卡尔曼滤波的收缩实现

Sau-Gee Chen, Jiann-Cherng Lee, Chieh-Chih Li
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引用次数: 12

摘要

针对三种流行的卡尔曼滤波算法,提出了几种新的实时收缩实现。这些结构都由两个收缩阵列单元组成,其中第一个是基于三个新的收缩阵列,用于矩阵乘法和加法,而第二个是用于矩阵反转的传统收缩阵列。三种卡尔曼滤波算法的数学公式被安排为这些收缩阵列的最佳部署。这就产生了九个新的收缩卡尔曼滤波器。其中,在现有体系结构中,一个在速度和硬件复杂性方面都具有最佳性能。具体来说,该架构的0 (2n/sup 2/)个pe的数量比最知名结构的0 (2.5n/sup 2/)个pe要少,并且具有最高的吞吐率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Systolic implementation of Kalman filter
Several new real-time systolic implementations for three popular Kalman filtering algorithms are presented. These architectures are all composed of two units of systolic arrays, where the first one is based on three new, systolic arrays for matrix multiplications and additions, while the second one is a conventional systolic array for matrix inversion. Mathematical formulations of the three Kalman filtering algorithms are scheduled for the best deployment of those systolic arrays. This results in nine new systolic Kalman filters. Among them, one has the best performances in both speed and hardware complexities among the existing architectures. Specifically, this architecture has a smaller number of O(2n/sup 2/) PEs than O(2.5n/sup 2/) PEs of the best known structures, and a highest throughput rate.
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