Tensor-Based Array Processing Models, Applications and Algorithms

Tensor-Based Array Processing Models, Applications and Algorithms | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 20 March 2026 V1 Latest version Share on Tensor-Based Array Processing Models, Applications and Algorithms Authors : Carlos Pacca 0009-0007-7691-0069 [email protected] , Carlos A Pacca , Tomás C Fillo , and Marques J Pilar Authors Info & Affiliations https://doi.org/10.22541/au.177402901.18916761/v1 62 views 56 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract This report reviews tensor decompositions used in signal processing (SP) from a technical, algorithmic, and historical perspective, focusing on array-processing. We summarize CP/PARAFAC and structured variants for wireless, radar, and RIS/IRS channel estimation; highlight DOA/DOD and passive localization use cases; and cover Tucker and t-SVD tools for hyperspectral fusion and low-rank regularization. Algorithmic coverage includes MTTKRP-based CP-ALS, constrained/structured updates, and scalable trends such as randomized/sketched and parallel kernels. We also comment on identifiability discussion (Kruskal-type conditions, generic rank, typical ranks), degeneracy and ill-conditioning, complexity, and structured-factor updates. A chronology and reference map organize representative works, with applications spanning MIMO-OFDM, mmWave, THz, coprime arrays, and vector-sensor settings. Supplementary Material File (tensor-based models for array processing.pdf) Download 609.70 KB Information & Authors Information Version history V1 Version 1 20 March 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords als array processing cp/parafac doa estimation identifiability mimo mmwave radar ris/irs structured cp tensor decomposition tucker Authors Affiliations Carlos Pacca 0009-0007-7691-0069 [email protected] View all articles by this author Carlos A Pacca Academic at University of Guadalajara View all articles by this author Tomás C Fillo Academic at University of Guadalajara View all articles by this author Marques J Pilar Academic at University of Guadalajara View all articles by this author Metrics & Citations Metrics Article Usage 62 views 56 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Carlos Pacca, Carlos A Pacca, Tomás C Fillo, et al. Tensor-Based Array Processing Models, Applications and Algorithms. Authorea . 20 March 2026. DOI: https://doi.org/10.22541/au.177402901.18916761/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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