{"ID":2868373,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.16585","arxiv_id":"2509.16585","title":"Robust Sparse Subspace Tracking from Corrupted Data Observations","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.","short_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...","url_abs":"https://arxiv.org/abs/2509.16585","url_pdf":"https://arxiv.org/pdf/2509.16585v1","authors":"[\"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\"]","published":"2025-09-20T09:17:17Z","proceeding":"eess.SP","tasks":"[\"eess.SP\",\"cs.IT\"]","methods":"[]","has_code":false}