Robust subspace tracking in impulsive noise

Abstract

Subspace tracking is an efficient method to reduce the complexity of signal subspace estimation. Recursive least square-based (RLS) subspace tracking algorithm such as the PAST algorithm is attractive because they estimate the signal subspace adaptively and continuously and the computational complexity is relatively low. Unfortunately, the RLS algorithm is well known to be very sensitive to impulse noise and it's performance can degraded substantially. In this paper, a robust PAST algorithm, based on the concept of robust statistics, is proposed. The robustness is achieved by making the underlying RLS iteration more robust to impulse interference. This new method is also applicable to other RLS-based algorithms. In particular, a robust statistic based impulsive noise detector is incorporated into the subspace tracking algorithm. The impulses in the input data vector are detected, and they are prevented from corrupting the estimated subspace for further tracking. We also propose a new restoring mechanism to handle long burst of consecutive impulses, which is a very difficult problem to handle in practice. Simulation results show the proposed algorithm offers satisfactory robustness against individual and consecutive impulses, while the PAST algorithm degrades dramatically in similar impulse noise environment. For nominal Gaussian noise, the proposed robust subspace tracking algorithm offers similar performance as the PAST algorithm.