A parallel architecture for Kalman filter measurement update and parameter estimation
Automatica (Journal of IFAC)
Regular interactive algorithms and their implementations on processor arrays
Regular interactive algorithms and their implementations on processor arrays
A singular value decomposition updating algorithm for subspace tracking
SIAM Journal on Matrix Analysis and Applications
Partitioning of processor arrays: a piecewise regular approach
Integration, the VLSI Journal - Special issue on algorithms and architectures
Accurate Downdating of Least Squares Solutions
SIAM Journal on Matrix Analysis and Applications
On recursive least squares filtering algorithms and implementations
On recursive least squares filtering algorithms and implementations
A linear systolic array for recursive least squares
IEEE Transactions on Signal Processing
A unified co-processor architecture for matrix decomposition
Journal of Computer Science and Technology
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In this paper we survey a number of linear algebra algorithms that are used in many modern real-time high-throughput applications. We focus in particular on algorithms that are used in applications as diverse as adaptive filtering/beamforming, communications, signal/array processing, control, etc. We first consider least-squares estimation, QR decomposition, singular value decomposition, recursive least-squares estimation, and Kalman filtering algorithms. In particular, the QR decomposition which is used in least-squares solutions of linear system of equations, also forms one component of more complex algorithms such as Kalman filtering and the singular value decomposition. Systolic arrays, due to their modularity and regularity, are perfect matches for all the algorithms considered in this paper. All these problems are defined and described alongside the systolic algorithms and the architectures that implement them.