Abstract
A novel regularised algorithm is presented for the adaptive recursive least-squares Ladder-Lattice prediction and filtering. The design of the filter is based on formulating the Lattice section as a special implementation of the factorised recursive least-squares algorithm, assuming the data and the parameter tracking method meet certain specific criteria.
The proposed estimator provides on a linear array of n processors a data throughput comparable with the triangular systolic implementation of the recursive square root information filters on n(n - 1)/2 processors.
Unlike the standard Recursive Least Squares Lattice-Ladder algorithm the proposed regularised estimator parameter tracking technique can be interpreted as introduction of a set of forgetting factors tuned to control the character of the adaptation. The estimator reduces considerably the numerical problems related to overparametrisation or poor data.
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Kadlec, J., Gaston, F.M.F., Irwin, G.W. (1993). Regularised Lattice-Ladder Adaptive Filter. In: Kárný, M., Warwick, K. (eds) Mutual Impact of Computing Power and Control Theory. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-2968-2_18
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DOI: https://doi.org/10.1007/978-1-4615-2968-2_18
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