{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:GRRDNNRLXDUHVPFA4C4T6WDEOD","short_pith_number":"pith:GRRDNNRL","schema_version":"1.0","canonical_sha256":"346236b62bb8e87abca0e0b93f586470e133ddbd2bc14bf7f623a084e5500856","source":{"kind":"arxiv","id":"2305.00384","version":3},"attestation_state":"computed","paper":{"title":"Dynamic and Robust Sensor Selection Strategies for Wireless Positioning with TOA/RSS Measurement","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Christopher G. Brinton, David J. Love, James V. Krogmeier, Myeung Suk Oh, Seyyedali Hosseinalipour, Taejoon Kim","submitted_at":"2023-04-30T04:36:30Z","abstract_excerpt":"Emerging wireless applications are requiring ever more accurate location-positioning from sensor measurements. In this paper, we develop sensor selection strategies for 3D wireless positioning based on time of arrival (TOA) and received signal strength (RSS) measurements to handle two distinct scenarios: (i) known approximated target location, for which we conduct dynamic sensor selection to minimize the positioning error; and (ii) unknown approximated target location, in which the worst-case positioning error is minimized via robust sensor selection. We derive expressions for the Cram\\'er-Rao"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2305.00384","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2023-04-30T04:36:30Z","cross_cats_sorted":[],"title_canon_sha256":"fd7feaa63debb8f9986c867da42916d57ab3a9ccfebacaedfd0f4d926d0c92a9","abstract_canon_sha256":"9b0877981f3d40be70f95981d03108759cd45b17d74f832cb5e73908d5936dea"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-25T01:17:42.277669Z","signature_b64":"LUiNrjr303PmbI4dTgWlIqW2HrmfuCSmIFmFnLmnfJf94wvSdyCuDvskaFvGtfhVQmhubnfLkb6bQBeR6XciAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"346236b62bb8e87abca0e0b93f586470e133ddbd2bc14bf7f623a084e5500856","last_reissued_at":"2026-06-25T01:17:42.277148Z","signature_status":"signed_v1","first_computed_at":"2026-06-25T01:17:42.277148Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Dynamic and Robust Sensor Selection Strategies for Wireless Positioning with TOA/RSS Measurement","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Christopher G. Brinton, David J. Love, James V. Krogmeier, Myeung Suk Oh, Seyyedali Hosseinalipour, Taejoon Kim","submitted_at":"2023-04-30T04:36:30Z","abstract_excerpt":"Emerging wireless applications are requiring ever more accurate location-positioning from sensor measurements. In this paper, we develop sensor selection strategies for 3D wireless positioning based on time of arrival (TOA) and received signal strength (RSS) measurements to handle two distinct scenarios: (i) known approximated target location, for which we conduct dynamic sensor selection to minimize the positioning error; and (ii) unknown approximated target location, in which the worst-case positioning error is minimized via robust sensor selection. We derive expressions for the Cram\\'er-Rao"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.00384","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2305.00384/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2305.00384","created_at":"2026-06-25T01:17:42.277209+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.00384v3","created_at":"2026-06-25T01:17:42.277209+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.00384","created_at":"2026-06-25T01:17:42.277209+00:00"},{"alias_kind":"pith_short_12","alias_value":"GRRDNNRLXDUH","created_at":"2026-06-25T01:17:42.277209+00:00"},{"alias_kind":"pith_short_16","alias_value":"GRRDNNRLXDUHVPFA","created_at":"2026-06-25T01:17:42.277209+00:00"},{"alias_kind":"pith_short_8","alias_value":"GRRDNNRL","created_at":"2026-06-25T01:17:42.277209+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GRRDNNRLXDUHVPFA4C4T6WDEOD","json":"https://pith.science/pith/GRRDNNRLXDUHVPFA4C4T6WDEOD.json","graph_json":"https://pith.science/api/pith-number/GRRDNNRLXDUHVPFA4C4T6WDEOD/graph.json","events_json":"https://pith.science/api/pith-number/GRRDNNRLXDUHVPFA4C4T6WDEOD/events.json","paper":"https://pith.science/paper/GRRDNNRL"},"agent_actions":{"view_html":"https://pith.science/pith/GRRDNNRLXDUHVPFA4C4T6WDEOD","download_json":"https://pith.science/pith/GRRDNNRLXDUHVPFA4C4T6WDEOD.json","view_paper":"https://pith.science/paper/GRRDNNRL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.00384&json=true","fetch_graph":"https://pith.science/api/pith-number/GRRDNNRLXDUHVPFA4C4T6WDEOD/graph.json","fetch_events":"https://pith.science/api/pith-number/GRRDNNRLXDUHVPFA4C4T6WDEOD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GRRDNNRLXDUHVPFA4C4T6WDEOD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GRRDNNRLXDUHVPFA4C4T6WDEOD/action/storage_attestation","attest_author":"https://pith.science/pith/GRRDNNRLXDUHVPFA4C4T6WDEOD/action/author_attestation","sign_citation":"https://pith.science/pith/GRRDNNRLXDUHVPFA4C4T6WDEOD/action/citation_signature","submit_replication":"https://pith.science/pith/GRRDNNRLXDUHVPFA4C4T6WDEOD/action/replication_record"}},"created_at":"2026-06-25T01:17:42.277209+00:00","updated_at":"2026-06-25T01:17:42.277209+00:00"}