Conjugate Gradient Adaptive Learning with Tukey's Biweight M-Estimate
This work addresses system identification and active noise control in impulsive noise environments, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles system identification in impulsive noise environments by proposing the TbMCG algorithm, which achieves faster convergence and reduced computational complexity compared to the RLS algorithm, as confirmed by simulation results.
We propose a novel M-estimate conjugate gradient (CG) algorithm, termed Tukey's biweight M-estimate CG (TbMCG), for system identification in impulsive noise environments. In particular, the TbMCG algorithm can achieve a faster convergence while retaining a reduced computational complexity as compared to the recursive least-squares (RLS) algorithm. Specifically, the Tukey's biweight M-estimate incorporates a constraint into the CG filter to tackle impulsive noise environments. Moreover, the convergence behavior of the TbMCG algorithm is analyzed. Simulation results confirm the excellent performance of the proposed TbMCG algorithm for system identification and active noise control applications.