{"id":3514,"date":"2025-09-17T04:00:20","date_gmt":"2025-09-17T04:00:20","guid":{"rendered":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?page_id=3514"},"modified":"2025-09-17T04:00:20","modified_gmt":"2025-09-17T04:00:20","slug":"rf-quantum-scythe-2","status":"publish","type":"page","link":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/?page_id=3514","title":{"rendered":"RF Quantum SCYTHE"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">We present RF Quantum SCYTHE, a modular<br>framework for reproducible passive RF geolocation research. Unlike siloed studies that focus on individual algorithms, SCYTHE<br>provides a comprehensive suite of interoperable demonstrations<br>covering trajectory recovery, sensor fusion, adaptive denoising, reinforcement learning, and hybrid triangulation. Each<br>module produces standardized JSON summaries and LaTeXready figures\/tables, enabling direct integration into publications<br>and downstream analysis pipelines. Together, these components<br>form a systems-level testbed for evaluating geolocation algorithms across diverse modalities, noise conditions, and decision<br>policies. We demonstrate four core modules\u2014AoA sequence<br>recovery, AoA+TDoA fusion, policy-driven denoising, and hybrid triangulation\u2014achieving 25-45% error reductions in multisensor tracking and up to 91.6% RMSE improvements in triangulation accuracy. The framework lowers barriers to entry for<br>reproducible RF research and provides a standardized baseline<br>for future extensions, including machine-learned policies and<br>multi-emitter geolocation scenarios.<\/p>\n\n\n\n<div data-wp-interactive=\"core\/file\" class=\"wp-block-file\"><object data-wp-bind--hidden=\"!state.hasPdfPreview\" hidden class=\"wp-block-file__embed\" data=\"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/wp-content\/uploads\/2025\/09\/RF-Quantum-SCYTHE-1.pdf\" type=\"application\/pdf\" style=\"width:100%;height:600px\" aria-label=\"Embed of RF Quantum SCYTHE.\"><\/object><a id=\"wp-block-file--media-7941a062-bd5d-46b6-95d9-91e3e5901d13\" href=\"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/wp-content\/uploads\/2025\/09\/RF-Quantum-SCYTHE-1.pdf\">RF Quantum SCYTHE<\/a><a href=\"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/wp-content\/uploads\/2025\/09\/RF-Quantum-SCYTHE-1.pdf\" class=\"wp-block-file__button wp-element-button\" download aria-describedby=\"wp-block-file--media-7941a062-bd5d-46b6-95d9-91e3e5901d13\">Download<\/a><\/div>\n","protected":false},"excerpt":{"rendered":"<p>We present RF Quantum SCYTHE, a modularframework for reproducible passive RF geolocation research. Unlike siloed studies that focus on individual algorithms, SCYTHEprovides a comprehensive suite of interoperable demonstrationscovering trajectory recovery, sensor fusion, adaptive denoising, reinforcement learning, and hybrid triangulation. Eachmodule produces standardized JSON summaries and LaTeXready figures\/tables, enabling direct integration into publicationsand downstream analysis pipelines.&hellip;&nbsp;<\/p>\n","protected":false},"author":2,"featured_media":1698,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"neve_meta_sidebar":"","neve_meta_container":"","neve_meta_enable_content_width":"","neve_meta_content_width":0,"neve_meta_title_alignment":"","neve_meta_author_avatar":"","neve_post_elements_order":"","neve_meta_disable_header":"","neve_meta_disable_footer":"","neve_meta_disable_title":"","footnotes":""},"class_list":["post-3514","page","type-page","status-publish","has-post-thumbnail","hentry"],"_links":{"self":[{"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/pages\/3514","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=3514"}],"version-history":[{"count":0,"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/pages\/3514\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/media\/1698"}],"wp:attachment":[{"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3514"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}