A variable leaky LMS adaptive algorithm

The LMS algorithm has found wide application in many areas of adaptive signal processing and control. We introduce a variable leaky LMS algorithm, designed to overcome the slow convergence of standard LMS in cases of high input eigenvalue spread. The algorithm uses a greedy punish/reward heuristic together with a quantized leak adjustment function to vary the leak. Simulation results confirm that the new algorithm can significantly outperform standard LMS when the input eigenvalue spread is high.