A continuous-time linear system identification method for slowly sampled data
IEEE Transactions on Signal Processing
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Most shift operator-based adaptive algorithms exhibit poor numerical behavior when the input discrete time process is obtained from a continuous time process by fast sampling. This includes the shift operator based least squares lattice algorithm. We develop a delta least squares lattice algorithm. This algorithm has a low computational complexity compared with the delta Levinson RLS algorithm and shows better numerical properties compared with the shift least squares lattice algorithm under fast sampling. Computer simulations show that the new algorithm also outperforms an existing delta least squares lattice algorithm