Robust DCD-Based Recursive Adaptive Algorithms
This work addresses adaptive filtering for channel identification in impulsive noise, offering incremental improvements in computational efficiency and robustness.
The authors tackled the problem of recursive least squares adaptive filtering in impulsive noise scenarios by generalizing the dichotomous coordinate descent algorithm and incorporating robust strategies, resulting in novel computationally efficient algorithms with a variable forgetting factor for tracking abrupt changes, as demonstrated in channel identification simulations.
The dichotomous coordinate descent (DCD) algorithm has been successfully used for significant reduction in the complexity of recursive least squares (RLS) algorithms. In this work, we generalize the application of the DCD algorithm to RLS adaptive filtering in impulsive noise scenarios and derive a unified update formula. By employing different robust strategies against impulsive noise, we develop novel computationally efficient DCD-based robust recursive algorithms. Furthermore, to equip the proposed algorithms with the ability to track abrupt changes in unknown systems, a simple variable forgetting factor mechanism is also developed. Simulation results for channel identification scenarios in impulsive noise demonstrate the effectiveness of the proposed algorithms.