{"paper":{"title":"Compressive Detection of Random Subspace Signals","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Alireza Razavi, Danijela Cabric, Mikko Valkama","submitted_at":"2015-07-10T19:37:53Z","abstract_excerpt":"The problem of compressive detection of random subspace signals is studied. We consider signals modeled as $\\mathbf{s} = \\mathbf{H} \\mathbf{x}$ where $\\mathbf{H}$ is an $N \\times K$ matrix with $K \\le N$ and $\\mathbf{x} \\sim \\mathcal{N}(\\mathbf{0}_{K,1},\\sigma_x^2 \\mathbf{I}_K)$. We say that signal $\\mathbf{s}$ lies in or leans toward a subspace if the largest eigenvalue of $\\mathbf{H} \\mathbf{H}^T$ is strictly greater than its smallest eigenvalue. We first design a measurement matrix $\\mathbf{\\Phi}=[\\mathbf{\\Phi}_s^T,\\mathbf{\\Phi}_o^T]^T$ comprising of two sub-matrices $\\mathbf{\\Phi}_s$ and $"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1507.02999","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}