Performance improvements of adaptive FIR filters using adjusted step size LMS algorithm

In this paper possible improvements in the performance of adaptive FIR filters in nonstationary environments are reported. Nonstationary environments means that the statistical property of the noise path are time varying such as HF channel time variations. A modified LMS algorithm incorporating a recursively adjusted adaptation step size based on rough estimate of the performance surface gradient square are proposed, and shows superior performance in the nonstationary environments. These proposed algorithms could be a promising algorithms for a variety of applications such as tracking the HF channel that is used to provide high data rate communications over a nominal 3 kHz bandwidth HF channel, adaptive noise canceling, line enhancement, etc. Beside of the good behavior of maintaining the trade off between misadjustment and tracking ability, the algorithm requires less computations making the practical real time application attractive