2022/10/31 by Joan Palacios, Palacios, Joan, Nuria González‐Prelcic +1 · 1 citation
Engineering · #FOS: Electrical engineering #Indoor and Outdoor Localization Technologies #Microwave Imaging and Scattering Analysis #Signal Processing (eess.SP) #Sparse and Compressive Sensing Techniques #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2210.17450
openalex publication_date 2022/10/31 · openalex created_date 2022/11/06 · openalex updated_date 2026/07/28
Greedy sparse recovery has become a popular tool in many applications, although its complexity is still prohibitive when large sparsifying dictionaries or sensing matrices have to be exploited. In this paper, we formulate first a new class of sparse recovery problems that exploit multidimensional dictionaries and the separability of the measurement matrices that appear in certain problems. Then we develop a new algorithm, Separable Multidimensional Orthogonal Matching Pursuit (SMOMP), which can solve this class of problems with low complexity. Finally, we apply SMOMP to the problem of joint localization and communication at mmWave, and numerically show its effectiveness to provide, at a reasonable complexity, high accuracy channel and position estimations.