Search

Underwater acoustic channels often have to face the interference of impulsive noise, which is usually modeled by α-stable distribution in simulation experiments. To solve the problem of underwater acoustic channel estimation under impulsive noise, this paper proposes a convex combination–variable-step-size least mean p-norm algorithm. The algorithm incorporates a convex combination into the variable-step-size least mean p-norm algorithm and uses the convex combination of different convergence domains provided by changing the parameters of the Gaussian function to further improve the effect after convergence. The simulation results of channel estimation show that the convex combination–variable-step-size least mean p-norm algorithm provides a more stable, robust, and universal solution than the variable-step-size least mean p-norm algorithm. [ABSTRACT FROM AUTHOR]

For the channel estimation problem under α-stable distributed impulse interference, the traditional fixed-step adaptive filtering cannot satisfy the fast convergence speed and low steady-state error at the same time, whereas the variable-step method is able to effectively solve this contradiction. This paper proposes an improved variable step-size least mean p-power adaptive algorithm that offers good robustness against impulsive noise. The proposed algorithm takes into account the linkage between the errors and uses the adjustment of the step size based on the errors of the current moment and the previous k moments, thus overcoming the problems of poor anti-noise performance and large steady-state fluctuations of the fixed-step size algorithm. This algorithm ensures that the step size does not change abruptly when the system is disturbed by impulse noise and can achieve a lower steady-state error. The simulation results show that the algorithm has better convergence than the traditional fixed step-size algorithm and the existing variable step-size algorithm under the interference of impulsive noise. [ABSTRACT FROM AUTHOR]