Robust Quasi-Newton Adaptive Filtering Algorithms

Two robust quasi-Newton (QN) adaptive filtering algorithms that perform well in impulsive-noise environments are proposed. The new algorithms use an improved estimate of the inverse of the autocorrelation matrix and an improved weight-vector update equation, which lead to improved speed of convergence and steady-state misalignment relative to those achieved in the known QN algorithms. A stability analysis shows that the proposed algorithms are asymptotically stable. The proposed algorithms perform data-selective adaptation, which significantly reduces the amount of computation required. Simulation results presented demonstrate the attractive features of the proposed algorithms.