Robust Bias-Compensated CR-NSAF Algorithm: Design and Performance Analysis

The censored regression (CR)-based normalized subband adaptive algorithm (CR-NSAF) model has been recently introduced for processing signals with censored data. However, the effectiveness of this algorithm declines when dealing with noisy input signals in impulsive noise environments. To resolve this challenge, we propose a robust bias-compensated CR-NSAF algorithm (RBC-CRNSAF). This algorithm alleviates the negative impacts of the CR system and improves robustness by employing a logarithmic cost function approach. It also minimizes estimation bias from input noise by incorporating new compensation terms into the weights update function. Additionally, we analyze the computational complexity, convergence characteristics, and stability conditions of the algorithm. Finally, computer simulations indicate that RBC-CRNSAF considerably outperforms other similar algorithms in impulsive noise environments, validating its enhanced performance.