Total complex kernel risk-sensitive loss for robust DOA estimation

doi:10.1016/j.sigpro.2025.110002

Total complex kernel risk-sensitive loss for robust DOA estimation

2025-03-01

0

PRE

AI

DOI：10.1016/j.sigpro.2025.110002

Original

Original request for help

Save

Abstract

En 中文

Adaptive filtering-based approaches have been proposed for low-complexity direction of arrival (DOA) estimation. Nonetheless, existing methods demonstrate significant performance deterioration in bias compensation models that incorporate impulse noise. To address this challenge, we have introduced a novel similarity measure in kernel space, termed total complex kernel risk-sensitive loss (TCKRSL), which effectively extracts higher-order statistics from the data to mitigate the detrimental effects of outliers caused by impulse noise. Subsequently, we derived a robust adaptive filtering algorithm known as the minimum total complex kernel risk-sensitive loss (MTCKRSL) algorithm based on stochastic gradient descent and applied it to DOA estimation via adaptive nulling array antenna. To further enhance estimation performance, we implemented a variable step size (VSS) mechanism grounded in cumulative instantaneous error and the estimated signal power aimed at balancing the trade-off between steady-state error and convergence speed, resulting in the VSSMTCKRSL algorithm. Additionally, the convergence properties and computational complexity of the proposed algorithm were analyzed elaborately. Simulation results across various performance metrics demonstrate that the proposed VSS-MTCKRSL algorithm outperforms the state-of-the-art algorithms regardless of the presence of impulse noise or Gaussian noise.

Keywords:

DOA estimation

Risk-sensitive criterion

Variable step size

Adaptive nulling

Impulse noise

Total complex kernel risk-sensitive loss for robust DOA estimation

Abstract

Journal

Organization