论文信息 - Multichannel recursive least squares adaptive filtering without a desired signal

Multichannel recursive least squares adaptive filtering without a desired signal

The author presents a pair of adaptive QR decomposition-based algorithms for the adaptive mixed filter in which no desired signal is available, but the signal-to-data cross-correlation vector is known. The algorithms are derived by formulating the recursive mixed filter as a least-squares problem and then applying orthogonal QR-based techniques in its solution. This leads to algorithms with the performance, numerical, and structural advantages of the RLS/QR algorithm, but without the requirement of a desired signal. Both Givens and square-root-free Givens rotations are used in implementing the recursive QR decomposition. Because of their structural regularity, the algorithms are easily implemented by triangular systolic array structures. Simulations show that these algorithms require fewer computations and less precision than recursive sample matrix inversion approaches. >

Paul S. Lewis

[1] R.C. Johnson,et al. Introduction to adaptive arrays , 1982, Proceedings of the IEEE.

[2] H. T. Kung,et al. Matrix Triangularization By Systolic Arrays , 1982, Optics & Photonics.

[3] L. J. Griffiths,et al. A simple adaptive algorithm for real-time processing in antenna arrays , 1969 .

[4] J. G. McWhirter,et al. A novel algorithm and architecture for adaptive digital beamforming , 1986 .

[5] Sun-Yuan Kung,et al. Algorithms and architectures for adaptive least squares signal processing with applications in magnetoencephalography , 1988 .

[6] V. Voipio. Adaptive antennas , .

[7] J. G. McWhirter,et al. Recursive Least-Squares Minimization Using A Systolic Array , 1983, Optics & Photonics.

[8] P. S. Lewis,et al. RLS Adaptive Filtering Without A Desired Signal: QR Algorithms And Architectures , 1988, Twenty-Second Asilomar Conference on Signals, Systems and Computers.

[9] S. Haykin,et al. Adaptive Filter Theory , 1986 .