2017/01/11 by Manxi Wang, Wang, Manxi, Yongcheng Li +5
Computer Science · Engineering · #Distributed #Distributed Sensor Networks and Detection Algorithms #FOS: Computer and information sciences #Parallel #Sparse and Compressive Sensing Techniques #Target Tracking and Data Fusion in Sensor Networks #and Cluster Computing (cs.DC) #cs.DC
paper · pdf · doi:10.48550/arxiv.1701.03043
IEEE GlobalSIP 2016
arxiv created 2017/01/11 · openalex publication_date 2017/01/11 · arxiv updated 2017/01/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper considers the recovery of group sparse signals over a multi-agent network, where the measurements are subject to sparse errors. We first investigate the robust group LASSO model and its centralized algorithm based on the alternating direction method of multipliers (ADMM), which requires a central fusion center to compute a global row-support detector. To implement it in a decentralized network environment, we then adopt dynamic average consensus strategies that enable dynamic tracking of the global row-support detector. Numerical experiments demonstrate the effectiveness of the proposed algorithms.