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Table of Contents.

>Z< Systolic algorithms for adaptive signal processing M. Moonen

An overview is given of recent work in parallel algorithms development. It is shown how one specific type of systolic algorithm/array can be used for several 'classical' adaptive signal processing tasks, such as recursive least squares parameter estimation, SVD updating, Kalman filtering, beamforming and direction finding, etc.

>Z< A systolic array for recursive least squares computations. Part II: Mapping directionally weighted RLS on an SVD updating array M. Moonen

In an earlier paper, a systolic algorithm/array was derived for recursive least squares (RLS) estimation, which achieves an $O(n^0)$ throughput rate with $O(n^2)$ parallelism. The resulting array is specifically tuned towards the RLS problem. Here, a different route is taken, by trying to implement the RLS problem on a systolic array, which is also useful for several other applications, such as SVD updating and Kalman filtering. This is important in view of possible hardware implementation. An additional advantage is that, unlike the earlier array, it is now possible to incorporate alternative data weighting strategies, such as directional weighting, without sacrificing speed.

>Z< A systolic array for recursive least squares by inverse updating M. Moonen and J.G. McWhirter

A novel systolic array is described for recursive least squares estimation based on the method of 'inverse updating' or 'square root covariance updating'. The array is similar to the well known Gentlemen & Kung array for triangular updating, but unlike the latter, it performs a complete RLS computation. The array also achieves an $O(n^0)$ throughput rate with $O(n^2)$ parallelism, while the functionality of its cells remain remarkably simple.

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