Adaptive linear filtering using interior paint optimization techniques

We propose a novel approach for the linear adaptive filtering problem using techniques from interior point optimization, The main idea is to formulate a com ex feasibility problem at each iteration and obtain as an estimate a filter near the center of the feasible region. It is shown, under some mil d conditions, that this algorithm generates a sequence of filters convergin g to the optimum linear filter at the rate O(1/n), where n is the number of data samples. Furthermore, we show that the algorithm can be made recursiv e with a per-sample complexity of O(M-2.2), where M is the filter length. T he potential of the algorithm for practical applications is demonstrated vi a numerical simulations where the new algorithm is shown to have superior t ransient behavior and improved robustness to the source signal statistics w hen compared to the recursive least-squares (RLS) method.