Split Recursive Least Squares: Algorithms, Architectures, and Applications
MARYLAND UNIV COLLEGE PARK INST FOR SYSTEMS RESEARCH
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In this paper, a new computationally efficient algorithm for recursive least-squares RLS filtering is presented. The proposed Split RLS algorithm can perform the approximated RLS with 0N complexity for signals having no special data structure to be exploited, while avoiding the high computational complexity 0N2 required in the conventional RLS algorithms. Our performance analysis shows that the estimation bias will be small when the input data are less correlated. We also show that for highly correlated data, the orthogonal preprocessing scheme can be used to improve the performance of the Split RLS. Furthermore, the systolic implementation of our algorithm based on the QR-decomposition RLS QRD-RLS array as well as its application to multidimensional adaptive filtering is also discussed. The hardware complexity for the resulting array is only 0N and the system latency can be reduced to Olog2 N. The simulation results show that the Split RLS outperforms the conventional RLS in the application of image restoration. A major advantage of the Split RLS is its superior tracking capability over the conventional RLS under non-stationary environments.
- Numerical Mathematics
- Statistics and Probability