{"id":3473,"date":"2025-09-16T15:32:29","date_gmt":"2025-09-16T15:32:29","guid":{"rendered":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=3473"},"modified":"2025-09-16T15:32:29","modified_gmt":"2025-09-16T15:32:29","slug":"revolutionizing-rf-tracking-a-graph-based-breakthrough-in-aoa-only-trajectory-recovery","status":"publish","type":"post","link":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/?p=3473","title":{"rendered":"RF Sequence Recovery with Graph-Based Inference: An AoA-Only Approach"},"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=\"PPcbqdEvDO\"><a href=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?page_id=3467\">RF Sequence Recovery with Graph-Based Inference: An AoA-Only Approach<\/a><\/blockquote><iframe loading=\"lazy\" class=\"wp-embedded-content\" sandbox=\"allow-scripts\" security=\"restricted\" style=\"position: absolute; visibility: hidden;\" title=\"&#8220;RF Sequence Recovery with Graph-Based Inference: An AoA-Only Approach&#8221; &#8212; Spectrcyde\" src=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?page_id=3467&#038;embed=true#?secret=fdwxj1ZKfS#?secret=PPcbqdEvDO\" data-secret=\"PPcbqdEvDO\" width=\"600\" height=\"338\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\"><\/iframe>\n<\/div><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>RF Tracking: A Graph-Based Breakthrough in AoA-Only Trajectory Recovery<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Hey tech enthusiasts and signal processing geeks! Today, I&#8217;m diving into a fascinating new paper that&#8217;s pushing the boundaries of passive RF geolocation. Titled <em>&#8220;RF Sequence Recovery with Graph-Based Inference: An AoA-Only Approach&#8221;<\/em> by Benjamin J. Gilbert from College of the Mainland, this work tackles a tough problem in electronic warfare and surveillance: reconstructing the path of an RF emitter using only noisy, incomplete angle-of-arrival (AoA) data. No fancy time differences or signal strengths required\u2014just directions from a handful of sensors. Let&#8217;s break it down and see why this could be a game-changer.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">The Challenge: Tracking Ghosts in Noisy Skies<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Imagine you&#8217;re in a contested battlefield or monitoring spectrum in a busy urban area. RF emitters\u2014like drones, vehicles, or communication devices\u2014are moving around, but your sensors only catch glimpses. Traditional methods like triangulation need simultaneous pings from multiple sensors to pinpoint a location. Miss a few observations due to jamming, stealth tech, or just bad luck, and the whole system falls apart.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">AoA measurements give you the direction (angle) from a sensor to the emitter, but they&#8217;re often sparse (e.g., only 25% of possible data) and noisy (up to 10\u00b0 error from multipath interference or cheap hardware). Without extra info, reconstructing a full 2D trajectory is like solving a puzzle with half the pieces missing and some bent out of shape. Machine learning alternatives exist but demand tons of training data, which isn&#8217;t always available for new threats.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Gilbert&#8217;s paper steps in here, focusing on &#8220;AoA-only&#8221; recovery. It&#8217;s especially relevant for distributed sensor networks where syncing clocks for time-based methods (like TDoA) is impractical.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">The Innovation: Grid Graphs Meet Beam Search<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The core idea? Turn the surveillance area into a discrete grid graph and treat trajectory recovery as a path-finding problem. Here&#8217;s the high-level flow:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Discretize the Space<\/strong>: Divide the area (say, 5km x 5km) into a 50&#215;50 grid with 100m cells. Each cell is a node representing a possible emitter position.<\/li>\n\n\n\n<li><strong>Mobility Constraints<\/strong>: Connect nodes with edges weighted by a Gaussian mobility model. This favors smooth, realistic movements (short hops preferred) while allowing for some flexibility. No teleporting across the map!<\/li>\n\n\n\n<li><strong>Observation Likelihood<\/strong>: For each AoA measurement, calculate how likely it is that the emitter is in a given grid cell based on the expected angle from the sensor.<\/li>\n\n\n\n<li><strong>Beam Search Magic<\/strong>: Use a beam search algorithm to explore the most probable paths over time. It keeps the top K (e.g., 50) trajectory hypotheses at each step, updating scores with mobility probs and AoA likelihoods. If no observation at a time step? No problem\u2014it falls back on mobility to bridge the gap.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">This setup handles sparsity naturally: the graph &#8220;interpolates&#8221; missing data using movement rules. Computationally, it&#8217;s efficient\u2014O(TKD) complexity, where T is time steps (100 here), K is beam width, and D is node neighbors (about 8). On a modern CPU, it processes a 100-step sequence in just 45ms, making it real-time ready.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Pseudocode from the paper (Algorithm 1) shows the elegance: Initialize with uniform priors, extend paths with valid transitions, score them, and prune to the best K.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Proof in the Pudding: Experimental Results<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Gilbert tests on synthetic trajectories (random walks, circles, straight lines) over 100 steps, with 3 sensors in a triangle formation for good coverage. Key variables: observation fraction \u03c1 (0.25 to 1.0) and noise \u03c3\u03b8 (2\u00b0 to 12\u00b0).<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Sparse Observations<\/strong>: At \u03c1=0.25 (75% missing data), median position error is 370m\u2014still usable for a 5km area. At full \u03c1=1.0, it&#8217;s down to 160m. The system degrades gracefully, unlike triangulation which crashes without enough simultaneous hits.<\/li>\n\n\n\n<li><strong>Noisy Conditions<\/strong>: Mean error stays under 400m up to \u03c3\u03b8=10\u00b0. Beyond that, it rises, but handles real-world noise from basic interferometers.<\/li>\n\n\n\n<li><strong>Visuals<\/strong>: Figure 1 in the paper shows a recovered path hugging the ground truth despite 50% sparsity and 5\u00b0 noise. Figure 2 plots error vs. noise, with bars showing variability over 50 Monte Carlo trials.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Tables I and II quantify it all: e.g., at 50% observations and 10\u00b0 noise, mean error is 450m.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Compared to classical least-squares triangulation:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Ideal conditions: Triangulation edges it out slightly (140m vs. 180m mean error).<\/li>\n\n\n\n<li>Real-world mess: At \u03c1=0.5, it&#8217;s 520m vs. 290m (44% better). At \u03c3\u03b8=10\u00b0, 780m vs. 410m (47% improvement).<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Bottom line: This graph approach shines where others fail\u2014sparse, noisy regimes common in electronic warfare.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Why This Matters: From Battlefields to Borders<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">In denied environments (think jammed signals or stealth emitters), robust passive geolocation is crucial for spectrum monitoring, air traffic control, or border security. This method&#8217;s AoA-only focus means fewer sensors needed, lower costs, and easier deployment. It quantifies uncertainty via multiple hypotheses, which is gold for decision-makers.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Limitations? It&#8217;s single-emitter for now, assumes smooth mobility (not erratic adversaries), and grid resolution trades off accuracy vs. compute. Future tweaks could add TDoA, ML-learned mobility, or adaptive grids.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">As of September 2025, this feels timely with rising drone threats and 5G\/6G spectrum crowding. While not yet spotted in major journals (based on quick searches), it&#8217;s a solid foundation for operational tools.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The RF Quantum SCYTHE includes a clever blend of graph theory and sequential inference, making RF tracking more resilient. If you&#8217;re in signal processing or defense tech, check out the full paper\u2014it&#8217;s accessible and packed with math for the implementers. Kudos to the open-source community (acknowledged in the paper) for enabling this.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">What do you think? Could this scale to 3D for aerial threats? Drop a comment below!<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><em>Note: All stats and figures referenced from the paper. For the original, reach out to bgilbert2@com.edu.<\/em><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.researchgate.net\/publication\/260636322_Multiagent_Reinforcement_Learning_Based_Spectrum_Sensing_Policies_for_Cognitive_Radio_Networks\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.researchgate.net\/publication\/260636322_Multiagent_Reinforcement_Learning_Based_Spectrum_Sensing_Policies_for_Cognitive_Radio_Networks\" target=\"_blank\" rel=\"noreferrer noopener\">Multiagent Reinforcement Learning Based Spectrum Sensing &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.researchgate.net\/publication\/260636322_Multiagent_Reinforcement_Learning_Based_Spectrum_Sensing_Policies_for_Cognitive_Radio_Networks\" target=\"_blank\" rel=\"noreferrer noopener\">This paper proposes distributed multiuser multiband spectrum sensing policies for cognitive radio networks based on multiagent reinforcement &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.researchgate.net\/publication\/260636322_Multiagent_Reinforcement_Learning_Based_Spectrum_Sensing_Policies_for_Cognitive_Radio_Networks\" target=\"_blank\" rel=\"noreferrer noopener\">researchgate.net<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.arxiv.org\/list\/eess\/2024-02?skip=450&amp;show=2000\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.arxiv.org\/list\/eess\/2024-02?skip=450&amp;show=2000\" target=\"_blank\" rel=\"noreferrer noopener\">Electrical Engineering and Systems Science Feb 2024 &#8211; arXiv<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.arxiv.org\/list\/eess\/2024-02?skip=450&amp;show=2000\" target=\"_blank\" rel=\"noreferrer noopener\">Title: Control in Stochastic Environment with Delays: A Model-based Reinforcement Learning Approach &#8230; Adaptive Collaborative Inference Method Based on &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.arxiv.org\/list\/eess\/2024-02?skip=450&amp;show=2000\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv.org<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/faculty.washington.edu\/sbrunton\/databookRL.pdf\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/faculty.washington.edu\/sbrunton\/databookRL.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">[PDF] Data Driven Science &amp; Engineering &#8211; faculty.\u200bwashington.\u200bedu<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/faculty.washington.edu\/sbrunton\/databookRL.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">Reinforcement learning (RL) is a major branch of machine learning that is con- cerned with how to learn control laws and policies to interact with a complex.<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/faculty.washington.edu\/sbrunton\/databookRL.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">faculty.washington.edu<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.umassd.edu\/engineering\/ece\/graduate\/list-of-doctoral-dissertations--ms-theses\/\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.umassd.edu\/engineering\/ece\/graduate\/list-of-doctoral-dissertations--ms-theses\/\" target=\"_blank\" rel=\"noreferrer noopener\">List of Doctoral Dissertations &amp; MS Theses &#8211; UMass Dartmouth<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.umassd.edu\/engineering\/ece\/graduate\/list-of-doctoral-dissertations--ms-theses\/\" target=\"_blank\" rel=\"noreferrer noopener\">List of Doctoral Dissertations &amp; MS Theses. Doctoral Dissertations. MS Theses. Doctoral Dissertations. 2025-2020 | 2019-2010 | 2009-2000 | 1999-1997.<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.umassd.edu\/engineering\/ece\/graduate\/list-of-doctoral-dissertations--ms-theses\/\" target=\"_blank\" rel=\"noreferrer noopener\">umassd.edu<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/github.com\/Trustworthy-AI-Group\/Adversarial_Examples_Papers\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/github.com\/Trustworthy-AI-Group\/Adversarial_Examples_Papers\" target=\"_blank\" rel=\"noreferrer noopener\">Trustworthy-AI-Group\/Adversarial_Examples_Papers: A list &#8230; &#8211; GitHub<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/github.com\/Trustworthy-AI-Group\/Adversarial_Examples_Papers\" target=\"_blank\" rel=\"noreferrer noopener\">A complete list of papers about adversarial examples. It appears that the List of All Adversarial Example Papers has been experiencing crashes over the past &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/github.com\/Trustworthy-AI-Group\/Adversarial_Examples_Papers\" target=\"_blank\" rel=\"noreferrer noopener\">github.com<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.mdpi.com\/2079-9292\/14\/11\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.mdpi.com\/2079-9292\/14\/11\" target=\"_blank\" rel=\"noreferrer noopener\">Electronics, Volume 14, Issue 11 (June-1 2025) \u2013 214 articles<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.mdpi.com\/2079-9292\/14\/11\" target=\"_blank\" rel=\"noreferrer noopener\">This paper proposes a bitwise modular reduction design based on Dadda tree compression arrays, achieving higher parallelism through a strategy that combines &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.mdpi.com\/2079-9292\/14\/11\" target=\"_blank\" rel=\"noreferrer noopener\">mdpi.com<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/icml.cc\/virtual\/2024\/session\/35594\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/icml.cc\/virtual\/2024\/session\/35594\" target=\"_blank\" rel=\"noreferrer noopener\">Poster Session 4 &#8211; ICML 2025<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/icml.cc\/virtual\/2024\/session\/35594\" target=\"_blank\" rel=\"noreferrer noopener\">We propose a novel model-based reinforcement learning algorithm&#8212;Dynamics Learning and predictive control with Parameterized Actions (DLPA)&#8212;for &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/icml.cc\/virtual\/2024\/session\/35594\" target=\"_blank\" rel=\"noreferrer noopener\">icml.cc<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/ieeexplore.ieee.org\/iel5\/4273787\/4273788\/04273795.pdf\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/ieeexplore.ieee.org\/iel5\/4273787\/4273788\/04273795.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">Table of contents &#8211; IEEE Xplore<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/ieeexplore.ieee.org\/iel5\/4273787\/4273788\/04273795.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">Learning to Grasp in Unknown Environment by Reinforcement Learning and. Shaping. Nasser Rezzoug, Philippe Gorce, Alexandre Abellard, MohammedBen. Khelifa &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/ieeexplore.ieee.org\/iel5\/4273787\/4273788\/04273795.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">ieeexplore.ieee.org<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/proceedings.iclr.cc\/paper_files\/paper\/2025\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/proceedings.iclr.cc\/paper_files\/paper\/2025\" target=\"_blank\" rel=\"noreferrer noopener\">International Conference on Representation Learning 2025 (ICLR &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/proceedings.iclr.cc\/paper_files\/paper\/2025\" target=\"_blank\" rel=\"noreferrer noopener\">&#8230; Adaptive Noise Distributions for Denoising-based &#8230; HASARD: A Benchmark for Vision-Based Safe Reinforcement Learning in Embodied Agents Tristan Tomilin, Meng &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/proceedings.iclr.cc\/paper_files\/paper\/2025\" target=\"_blank\" rel=\"noreferrer noopener\">proceedings.iclr.cc<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.ismrm.org\/13\/ep07.htm\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.ismrm.org\/13\/ep07.htm\" target=\"_blank\" rel=\"noreferrer noopener\">ISMRM 21st Annual Meeting<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.ismrm.org\/13\/ep07.htm\" target=\"_blank\" rel=\"noreferrer noopener\">This work assesses the feasibility of using a retrospective, non-rigid motion-corrected averaging approach to reduce the impact of involuntary motion on &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.ismrm.org\/13\/ep07.htm\" target=\"_blank\" rel=\"noreferrer noopener\">ismrm.org<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/orcid.org\/0009-0006-2298-6538\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/orcid.org\/0009-0006-2298-6538\" target=\"_blank\" rel=\"noreferrer noopener\">ORCID<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/orcid.org\/0009-0006-2298-6538\" target=\"_blank\" rel=\"noreferrer noopener\">ORCID Please enable JavaScript to continue using this application.<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/orcid.org\/0009-0006-2298-6538\" target=\"_blank\" rel=\"noreferrer noopener\">orcid.org<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/build.com.edu\/uploads\/sitecontent\/files\/fiscal-affairs\/FY15-16_Accounts_Payable_Check_Register_-_College_of_the_Mainland.pdf\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/build.com.edu\/uploads\/sitecontent\/files\/fiscal-affairs\/FY15-16_Accounts_Payable_Check_Register_-_College_of_the_Mainland.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">[PDF] 09\/01\/2015 &#8211; PERIOD ENDING: 08 &#8211; College of the Mainland<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/build.com.edu\/uploads\/sitecontent\/files\/fiscal-affairs\/FY15-16_Accounts_Payable_Check_Register_-_College_of_the_Mainland.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">0102051 09\/03\/15 Reconciled 0000309 John J. Buffa. 70.20. 0102052 09 &#8230; 0105100 03\/10\/16 Reconciled 0119819 Benjamin A. Marshall. 65.00.<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/build.com.edu\/uploads\/sitecontent\/files\/fiscal-affairs\/FY15-16_Accounts_Payable_Check_Register_-_College_of_the_Mainland.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">build.com.edu<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>RF Tracking: A Graph-Based Breakthrough in AoA-Only Trajectory Recovery Hey tech enthusiasts and signal processing geeks! Today, I&#8217;m diving into a fascinating new paper that&#8217;s pushing the boundaries of passive RF geolocation. Titled &#8220;RF Sequence Recovery with Graph-Based Inference: An AoA-Only Approach&#8221; by Benjamin J. Gilbert from College of the Mainland, this work tackles a&hellip;&nbsp;<\/p>\n","protected":false},"author":2,"featured_media":3445,"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":[11],"tags":[],"class_list":["post-3473","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-signal_scythe"],"_links":{"self":[{"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/3473","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/types\/post"}],"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=3473"}],"version-history":[{"count":0,"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/3473\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/media\/3445"}],"wp:attachment":[{"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3473"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3473"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3473"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}