An adaptive Volterra filtering algorithm with reduced parameters and kernels combination

A Volterra adaptive filter based on discrete cosine transform (DCT) and kernels combination is proposed. In accordance with the problem that the computational complexity of Volterra adaptive filtering algorithm increases by power series, the quadratic kernels are transformed into a diagonal matrix by DCT, so that the complexity of the filtering algorithm is reduced. In addition, the correlativity of the input signals is reduced, too. And the same order kernels of the Volterra filter are taken parallel combination by the mixing parameters, so the performance of the algorithm is significantly improved. Simulation results show that the proposed algorithm has faster convergence rate, lower steady-state error, better tracking capabilities and better noise robustness.