This paper proposes a fast convergence algorithm for adaptive FIR filters with sparse taps. Coefficient values and positions are simultaneously controlled. The proposed algorithm consists of two stages： flat-delay estimation and tapposition control with a constraint. The flat-delay estimation is carried out by estimating the significant dispersive region of the impulse response. The constrained tap-position control is achieved by imposing a limit on the new-tap-position search. Simulation results show that the proposed algorithm reduces the convergence speed by up to 85% over the conventional algorithms for a white signal input. For a colored signal, it also converges in 40% of the convergence time by the conventional algorithms. The proposed algorithm is applicable to adaptive FIR filters which are to model a path with long flat delay, such as echo cancelers for satellite-link communications.

This paper proposes new algorithms for adaptive FIR filters. The proposed algorithms provide both fast convergence and small final misadjustment with an adaptive step size even under an interference to the error. The basic algorithm pays special attention to the interference which contaminates the error. To enhance robustness to the interference, it imposes a special limit on the increment/decrement of the step-size. The limit itself is also varied according to the step-size. The basic algorithm is extended for application to nonstationary signals. Simulation results with white signals show that the final misadjustment is reduced by up to 22 dB under severe observation noise at a negligible expense of the convergence speed. An echo canceler simulation with a real speech signal exhibits its potential for a nonstationary signal.