Adaptive fir filters with automatic length optimization by monitoring a normalized combination scheme

This paper presents a novel strategy of adaptive filtering which provides an automatic self-configuration of the filter structure in terms of memory length. By monitoring the adaptive mixing of a normalized combination of two competing filters with a different number of coefficients, an online estimate of the optimum filter length is obtained and used to dynamically scale the size of the employed filters. Furthermore, a more efficient, simplified version of this approach is proposed and shown to be equally effective while significantly reducing the required complexity. Experimental results for high-order real-world systems as well as stationary noise and speech signals demonstrate the good performance and the robust tracking behaviour of the outlined algorithms in the context of realistic system identification scenarios.