Modified Incremental LMS with Improved Stability via Convex Combination of Two Adaptive Filters

摘要

In distributed networks, the conventional incremental mode of cooperation between the nodes may suffer instability due to two major reasons: (1) large local errors due to accidental problems, and (2) instability due to link failure or noisy link. This causes error propagation through the entire network resulting in divergence. In this research, we propose a novel incremental least mean square algorithm with improved stability by employing convex combination of two filters. Adaptation of one filter is based on the estimate of the adjacent node (incremental type), while that of the other is based on the estimate of the current local node at previous time instant. These two filters are then fused together by using a suitable mixing parameter. An adaptive mixing parameter is further proposed for this convex combination, ensuing dynamic assignment of the weights for the two combining filters. Steady state excess mean square error is derived for the proposed convex combination, and simulations are presented to validate the proposed claims.