{"id":3600,"date":"2025-09-20T05:11:33","date_gmt":"2025-09-20T05:11:33","guid":{"rendered":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=3600"},"modified":"2025-09-20T05:14:03","modified_gmt":"2025-09-20T05:14:03","slug":"threat-layer-fusion-combining-rf-motion-and-intent-for-wearable-overlays","status":"publish","type":"post","link":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=3600","title":{"rendered":"Threat-Layer Fusion Combining RF Motion and Intent for Wearable Overlays"},"content":{"rendered":"\n<figure class=\"wp-block-embed is-type-wp-embed is-provider-spectrcyde wp-block-embed-spectrcyde\"><div class=\"wp-block-embed__wrapper\">\n<blockquote class=\"wp-embedded-content\" data-secret=\"eUOD7rOE4R\"><a href=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?page_id=3594\">Threat-Layer Fusion Combining RF Motion and Intent for Wearable Overlays<\/a><\/blockquote><iframe class=\"wp-embedded-content\" sandbox=\"allow-scripts\" security=\"restricted\" style=\"position: absolute; visibility: hidden;\" title=\"&#8220;Threat-Layer Fusion Combining RF Motion and Intent for Wearable Overlays&#8221; &#8212; Spectrcyde\" src=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?page_id=3594&#038;embed=true#?secret=fN6yLOLFV3#?secret=eUOD7rOE4R\" data-secret=\"eUOD7rOE4R\" width=\"600\" height=\"338\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\"><\/iframe>\n<\/div><\/figure>\n\n\n\n<p>Critical alerting in wearable AR hinges on correctly<br>classifying moving objects as benign or threatening. RF sensors<br>can track motion of drones, vehicles and personnel, but motion<br>alone suffers from false critical alerts when trajectories deviate<br>from simple heuristics. In aviation, false alerts and missed<br>separation events have been traced to trajectory prediction<br>errors[1], motivating improved fusion of motion and intent.<br>Recent work in autonomous driving demonstrates that jointly<br>reasoning about high-level behavior and long-term trajectories<br>improves prediction accuracy and reduces reaction time[2].<br>We propose Threat-Layer Fusion, a framework that combines<br>dynamic occupancy motion analysis (DOMA) trajectories with<br>heuristic and machine-learning intent classifiers to reduce false<br>critical alerts in AR overlays. Our prototype processes RF<br>motion tracks from a wearable radar, estimates intent (approach,<br>hover, depart) using trajectory features and a neural intent net,<br>and fuses these layers to prioritize alerts. Experiments on a<br>scenario catalogue with drones, vehicles and personnel show that<br>Threat-Layer Fusion reduces false critical alerts by 37 % while<br>incurring only 10 ms additional latency<\/p>\n\n\n\n<p><a href=\"https:\/\/mastodon.social\/@Bgilbert1984\">Benjamin J Gilbert (@Bgilbert1984@mastodon.social) &#8211; Mastodon<\/a><\/p>\n\n\n\n<p>.<a href=\"https:\/\/www.facebook.com\/benjamin.j.gilbert\">Facebook<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Critical alerting in wearable AR hinges on correctlyclassifying moving objects as benign or threatening. RF sensorscan track motion of drones, vehicles and personnel, but motionalone suffers from false critical alerts when trajectories deviatefrom simple heuristics. In aviation, false alerts and missedseparation events have been traced to trajectory predictionerrors[1], motivating improved fusion of motion and intent.Recent&hellip;&nbsp;<a href=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=3600\" rel=\"bookmark\"><span class=\"screen-reader-text\">Threat-Layer Fusion Combining RF Motion and Intent for Wearable Overlays<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":3595,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","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":""},"categories":[10],"tags":[],"class_list":["post-3600","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-signal_scythe"],"_links":{"self":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/3600","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=3600"}],"version-history":[{"count":2,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/3600\/revisions"}],"predecessor-version":[{"id":3602,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/3600\/revisions\/3602"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/media\/3595"}],"wp:attachment":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3600"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3600"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3600"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}