# Robust DCD-Based Recursive Adaptive Algorithms

## TL;DR

This paper extends the dichotomous coordinate descent (DCD) algorithm to robust recursive least squares (RLS) adaptive filtering in impulsive noise environments, introducing efficient algorithms with tracking capabilities for abrupt system changes.

## Contribution

It generalizes DCD-based RLS algorithms for impulsive noise and introduces a variable forgetting factor for tracking abrupt changes.

## Key findings

- Effective in impulsive noise channel identification
- Computationally efficient with robust noise handling
- Capable of tracking abrupt system changes

## Abstract

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.