| Hi-index | 35.68 |
A multistep size frequency-domain adaptive filter capable of tracking both stationary and nonstationary signals is proposed. This algorithm makes use of the simple structure of the least mean square error algorithm to update the filter coefficients and process the signal. It then proceeds further by incorporating a set of three step sizes and a knowledge-based strategy to select the best set of filter coefficients and the optimum step size iteratively. The main advantage of the MSS algorithm over the conventional LMS algorithm is that better performance is achieved without the knowledge of the input signal characteristics, such as signal power, degree of nonstationarity, signal-to-noise ratio, and stability bounds. Experimentally, the MSS algorithm is tested under various signal environments. The transient characteristics of the step sizes are found to be in agreement with previous theoretical studies of nonstationary characteristics of the LMS algorithm. In order to reduce the complexity, the conventional frequency-domain block LMS structure is also modified so that the MSS algorithm can be embedded more efficiently by exploiting the block structure