Subspace leaky LMS.

Brian D. Rigling, Philip Schniter

Abstract: The least mean squared (LMS) adaptive filtering algorithm may experience uncontrolled parameter drift when its input signal is not persistently exciting, leading to serious consequences when implemented with finite word-length. Though so-called "tap-leakage" modifications of LMS have been proposed to mitigate this drift, they inevitably introduce parameter bias which degrades mean-squared error performance. In this letter, we propose a novel algorithm which leaks only in the unexcited modes, thus introducing insignificant bias, while still retaining the low computational complexity of LMS.