{"paper":{"title":"D-Optimized Sampling Design for System Identification","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"Irregular sampling times optimized for D-optimality improve the accuracy of nonparametric frequency response estimates under nonperiodic multisine excitation.","cross_cats":["cs.SY","math.OC"],"primary_cat":"eess.SY","authors_text":"Enrico Dozzi, Rodrigo A. Gonz\\'alez, Tom Oomen","submitted_at":"2026-05-13T07:47:49Z","abstract_excerpt":"Traditional system identification with multisine inputs relies on uniform sampling and periodic excitation to preserve Fourier orthogonality and avoid spectral leakage, limiting its use in scenarios with irregular sampling or nonperiodic inputs. This work investigates continuous-time system identification under nonperiodic multisine excitation and nonuniform sampling. We develop a nonparametric frequency response function estimator suited to such conditions and design irregular sampling schemes that enhance the informativeness of measurements and reduce spectral leakage. The proposed sampling "},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"The proposed sampling scheme improve the statistical accuracy of system identification in settings where periodic excitation is impractical.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the nonparametric frequency response estimator remains unbiased and leakage-free when both the input is nonperiodic and the sampling is nonuniform.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"D-optimized irregular sampling improves statistical accuracy of frequency response estimation under nonperiodic multisine excitation and nonuniform sampling.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Irregular sampling times optimized for D-optimality improve the accuracy of nonparametric frequency response estimates under nonperiodic multisine excitation.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"41d6dca85356bca0c1121bd017f20302653a9e97cf36fc1bea080098f02de596"},"source":{"id":"2605.13120","kind":"arxiv","version":1},"verdict":{"id":"f03f626e-862a-4227-bc4a-36671d507985","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-14T18:57:28.615589Z","strongest_claim":"The proposed sampling scheme improve the statistical accuracy of system identification in settings where periodic excitation is impractical.","one_line_summary":"D-optimized irregular sampling improves statistical accuracy of frequency response estimation under nonperiodic multisine excitation and nonuniform sampling.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the nonparametric frequency response estimator remains unbiased and leakage-free when both the input is nonperiodic and the sampling is nonuniform.","pith_extraction_headline":"Irregular sampling times optimized for D-optimality improve the accuracy of nonparametric frequency response estimates under nonperiodic multisine excitation."},"references":{"count":24,"sample":[{"doi":"","year":2002,"title":"str\\\"om, K.J. and Bernhardsson, B.M. (2002). Comparison of Riemann and Lebesgue sampling for first order stochastic systems. In Proceedings of the 41st IEEE Conference on Decision and Control, 2002, v","work_id":"a6909f29-6a49-441f-bdb9-9efc36d631b7","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2011,"title":"Bombois, X., Gevers, M., Hildebrand, R., and Solari, G. (2011). Optimal experiment design for open and closed-loop system identification. Communications in Information and Systems, 11(3), 197--224","work_id":"fb58e965-d01a-4e83-827d-dd87d2e0e8b3","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2004,"title":"Boyd, S. and Vandenberghe, L. (2004). Convex Optimization. Cambridge University Press","work_id":"c7cccca5-265d-4689-b822-85a937e09303","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2008,"title":"Garnier, H., Wang, L., and Young, P.C. (2008). Direct identification of continuous-time models from sampled data: Issues, basic solutions and relevance. In Identification of continuous-time models fro","work_id":"dca6a82d-8400-4b9c-91a2-e9a22554d8c9","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2025,"title":"Gonz \\'a lez, R.A., Classens, K., Rojas, C.R., Oomen, T., and Hjalmarsson, H. (2025 a ). Finite sample MIMO system identification with multisine excitation: Nonparametric, direct, and two-step paramet","work_id":"fad9459e-4e36-4148-a5f1-bd264952de61","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":24,"snapshot_sha256":"d17551cb1d57ce0be4434eab789e10352fc4bde6d6bd2d3d57dfb6829c7a4b8e","internal_anchors":0},"formal_canon":{"evidence_count":2,"snapshot_sha256":"649dc02d055f85f46c101eea1ce6148684b99f0c6fe3223e79952e387a9596ce"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}