A robust, parallelizable, \(O(m)\), a posteriori recursive least squares algorithm for efficient adaptive filtering
The paper presents a new recursive least squares finite memory adaptive filtering algorithm. Its main properties are small tracking error, high speed of convergence and simple stabilizability. Furthermore, it can be easily parallelized, thus resulting in a particularly fast algorithm, and its stabilized version appears to be suitable also for nonstationary signals. The results of a few applications to real data are shown.
scientific article; zbMATH DE number 1632248