Robust Sparse Subspace Tracking from Corrupted Data Observations
Ta Giang Thuy Loan, Hoang-Lan Nguyen, Nguyen Thi Ngoc Lan, Do Hai Son, Tran Thi Thuy Quynh, Nguyen Linh Trung, Karim Abed-Meraim, Thanh Trung Le
公開日: 2025/9/20
Abstract
Subspace tracking is a fundamental problem in signal processing, where the goal is to estimate and track the underlying subspace that spans a sequence of data streams over time. In high-dimensional settings, data samples are often corrupted by non-Gaussian noises and may exhibit sparsity. This paper explores the alpha divergence for sparse subspace estimation and tracking, offering robustness to data corruption. The proposed method outperforms the state-of-the-art robust subspace tracking methods while achieving a low computational complexity and memory storage. Several experiments are conducted to demonstrate its effectiveness in robust subspace tracking and direction-of-arrival (DOA) estimation.