Robust Exponential Hyperbolic Sine Adaptive Filter for Impulsive Noise Environments

In recent years, the hyperbolic family of adaptive algorithms have been widely used to combat impulsive noise. The novel exponential hyperbolic sine adaptive filters (EHSAF) and the normalized exponential hyperbolic sine adaptive filter (NEHSAF) suitable for impulsive noise environments are proposed in this brief. The cost function is based on the exponential hyperbolic sine-based error function. The stability condition based on the learning rate and the steady-state analysis are investigated too. Additionally, a variable scheme for the scaling parameter is proposed to remove the tradeoff between convergence speed and steady-state excess mean square error (EMSE). The computational complexity is presented too. The simulation results in the context of unknown system identification and echo cancellation application have been performed to prove the performance improvement of the proposed algorithms.

Automated summary

This brief proposes two novel adaptive filters suitable for impulsive noise environments and investigates their stability condition and steady-state analysis. Additionally, a variable scheme is proposed to address the tradeoff between performance and error. Simulation results demonstrate the improved performance of the proposed algorithms.