Tracking of multivariate time-variant systems based on on-line variable selection
Tracking time-variant systems has been of great interest in many engineering fields. Specifically, when system statistics change both in space (multivariate) and time with a short stationary regime, conventional adaptive algorithms suffer from the trade-off between convergence rate and accuracy. In this paper, we propose a tracking system consisting of a linear adaptive system accompanied by an on-line variable selection algorithm that is based on the least angle regression algorithm. This algorithm explicitly employs local (in time) correlation between the input and the output of an unknown system to select a subset of input variables at every time step. Therefore, it enables the multivariate adaptive filter to track the temporal changes of correlated variables. Simulations involving tracking of multi-channel time-variant systems demonstrate superior performance of the proposed approach when compared with the conventional methods