Robust augmented space recursive least-constrained-squares algorithms

This paper proposes a novel augmented space robust adaptive filter by reusing the errors for online applications. First, a batched augmented space constrained model (ASCM) is constructed to combat nonGaussian noise. In ASCM, the errors are reused by k nearest neighbors (k -NN) estimation. Then, an augmented space recursive least -constrained -squares algorithm integrating with distance -based k -NN method (ARLCS-dk) is developed within the framework of ASCM for adaptive filtering. Finally, to curb the size of ever-growing error network, a sliding window ARLCS-dk (SW-ARLCS-dk) is proposed to reduce the computational burden. Theoretical analyses of excess mean square error (EMSE) and testing mean square error (TMSE) are carried out for performance evaluation. Examples on time -series prediction of simulated and real -world data are used to illustrate the advantages of proposed algorithms on robustness and prediction accuracy.