{"paper":{"title":"Orthogonal Least Squares with Integrated Information Theoretic Criteria for Joint Number of Targets and DoA Estimation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Integrating information theoretic criteria into orthogonal least squares enables joint estimation of the number of targets and their directions of arrival.","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Fran\\c{c}ois Horlin, Gilles Monnoyer, J\\'er\\^ome Louveaux, Martin Willame","submitted_at":"2026-05-07T13:08:43Z","abstract_excerpt":"We address the joint estimation of the number of targets and their direction-of-arrivals (DoAs) using antenna arrays. Target-number estimation can be formulated as a model-order selection problem and solved with the information theoretic criteria (ITC). The ITC minimize an objective function that balances a likelihood term and a complexity penalty. However, direct application of the ITC requires maximum-likelihood DoA estimates for each candidate model order, which is computationally prohibitive because it entails a multidimensional search over all angle combinations. To reduce complexity, man"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Numerical simulations show that the proposed hybrid ITC-OLS algorithm consistently outperforms both the other proposed variants and a baseline method from the literature.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That embedding the ITC penalty inside the OLS greedy steps preserves the statistical consistency of model-order selection without introducing bias from the sequential nature of the search.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Hybrid ITC-OLS algorithm integrates model-order selection into greedy DoA estimation and outperforms other variants and a literature baseline in simulations.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Integrating information theoretic criteria into orthogonal least squares enables joint estimation of the number of targets and their directions of arrival.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"ae7ae4483c044733c1645bc49de39ec0d2da66cada4ec40b6359e0c0d499826a"},"source":{"id":"2605.06198","kind":"arxiv","version":2},"verdict":{"id":"e8c3f7e9-331b-4b9a-984a-8b93bf2ec5d8","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-08T06:51:18.642335Z","strongest_claim":"Numerical simulations show that the proposed hybrid ITC-OLS algorithm consistently outperforms both the other proposed variants and a baseline method from the literature.","one_line_summary":"Hybrid ITC-OLS algorithm integrates model-order selection into greedy DoA estimation and outperforms other variants and a literature baseline in simulations.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That embedding the ITC penalty inside the OLS greedy steps preserves the statistical consistency of model-order selection without introducing bias from the sequential nature of the search.","pith_extraction_headline":"Integrating information theoretic criteria into orthogonal least squares enables joint estimation of the number of targets and their directions of arrival."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.06198/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"claim_evidence","ran_at":"2026-05-20T13:02:04.241044Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-20T08:35:26.880392Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_title_agreement","ran_at":"2026-05-19T19:01:19.197452Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T12:54:05.623597Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"f0af0834471593bb8c382224d805480ca4cc19ab287f363b7b65ccd41956fd73"},"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"}