[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-86523-en":3,"doc-seo-86523-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},86523,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Rigidity-Based Multi-UAV Trajectory Optimization for Rapid Cooperative Emergency Target Localization","Rapid and accurate emergency target localization is critical for mission outcomes in vehicular and public-safety wireless networks, yet GNSS, Wi-Fi, and cellular positioning can suffer from degraded accuracy, limited availability, and regulatory constraints. UAV-based cooperative localization uses airborne sensors to estimate target position, but existing Fisher information matrix (FIM) trajectory optimization can be less effective early in the mission due to uncertain target estimates and limited measurement diversity. The proposed rigidity-based method maximizes the smallest nonzero singular value of the rigidity matrix of the UAV–target sensing graph, improving conditioning and reducing ambiguity. A pruning strategy enables efficient real-time implementation; simulations show 32.9% faster search while meeting FCC horizontal emergency localization requirements, with demonstrated scalability, robustness to positioning errors and NLOS loss, low sensitivity to heading parameters, computational feasibility, low communication overhead, and stable degradation under sensing and navigation perturbations.","Rigidity-Based Multi-UAV Trajectory Optimization for Rapid Cooperative Emergency Target  \nLocalization  \nHalim Lee and Jiwon Seo, Senior Member, IEEE  \narXiv :2607 . 10933v1 [ ee ss . SY] 12 Jul 2026  \nAbstract—Reducing the response time required for accurate localization of emergency callers is a critical challenge in vehicular technology and public-safety networks, where timely and reliable positioning directly affects mission outcomes. Although mobile devices commonly rely on global navigation satellite systems (GNSS), Wi-Fi, or cellular positioning, their accuracy and availability can be degraded by signal reception conditions, infrastructure coverage, and regulatory constraints. Unmanned aerial vehicle (UAV)-based localization has therefore emerged asa promising alternative, where UAVs act as airborne sensors to cooperatively estimate the target position. However, existing Fisher information matrix (FIM)-based UAV trajectory optimization methods rely on the current target estimate and can be less effective in the early mission stage, when measurement diversity is limited and the estimate is highly uncertain. To address this problem, we propose a rigidity-based UAV trajectory optimization method that maximizes the smallest nonzero singular value of the rigidity matrix associated with the UAV–target sensing graph, thereby improving geometric conditioning and reducing position ambiguity. We further introduce a pruning-based rigidity matrix reduction strategy for efficient real-time implementation. Simulation results show that the proposed method reduces search time by 32.9% compared with existing FIM-based methods while satisfying the Federal Communications Commission (FCC) horizontal emergency localization requirement within a shorter timeframe. Additional evaluations confirm its scalability, robustness to UAV positioning errors and non-line-of-sight (NLOS) path loss, low sensitivity to heading-angle parameters, computational feasibility, low communication overhead, and more stable degradation than proximal policy optimization (PPO)-based learning baselines under severe sensing and navigation perturbations.  \nIndex Terms—Target localization, cooperative UAVs, trajectory optimization, rigidity, RSS-based positioning, public-safety communications  \nCopyright (c) 2026 IEEE. Personal use of this material is permitted. However, permission to use this material for any other purposes must be obtained from the IEEE by sending a request to [pubs-permissions@ieee.org](pubs-permissions@ieee.org).  \nManuscript received March 00, 2026;  \nThis work was supported in part by the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT (MSIT), under Grants RS-2024-00358298 and RS-2025-25393156; in part by the Korea Aerospace Administration (KASA), under Grant RS-2022-NR067078; in part by Grant RS-2024-00407003 from the “Development of Advanced Technology for Terrestrial Radionavigation System” project, funded by the Ministry of Oceans and Fisheries, Republic of Korea; in part by the Unmanned Vehicles Core Technology Research and Development Program through the NRF and the Unmanned Vehicle Advanced Research Center (UVARC), funded by MSIT, Republic of Korea, under Grant RS-2020-NR046546; and in part by the Institute of Information & Communications Technology Planning & Evaluation (IITP) through the Information Technology Research Center (ITRC) program, funded by MSIT, under Grant IITP-2025-RS-2024- 00437494. (Corresponding Author: Jiwon Seo.)  \nHalim Lee and Jiwon Seo are with the School of Integrated Technology, Yonsei University, Incheon 21983, Republic of Korea (e-mail: [halim.lee@yonsei.ac.kr](halim.lee@yonsei.ac.kr); [jiwon.seo@yonsei.ac.kr](jiwon.seo@yonsei.ac.kr)).  \nI. INTRODUCTION  \nTHE rapid and accurate localization of a target in emer  \ngency situations is a critical requirement in vehicular and mission-critical wireless systems, where reliable positioning directly affects safety-of-life services. In ","cbCaiktNKvJ5ImHw","https://ap.wps.com/l/cbCaiktNKvJ5ImHw","pdf",9138812,4,1,20,"English","en",105,"# Introduction\n## Problem motivation and FCC requirements\n## Limitations of GNSS and fixed-infrastructure positioning\n## UAV-based cooperative localization as an alternative","[{\"question\":\"Why is rapid emergency target localization considered critical in public-safety networks?\",\"answer\":\"Reliable and timely positioning directly affects safety-of-life services and mission outcomes, and even small delays in obtaining a dependable location can have severe consequences.\"},{\"question\":\"What limitation affects existing FIM-based UAV trajectory optimization methods?\",\"answer\":\"They depend on the current target estimate and can perform poorly in the early mission stage when measurement diversity is limited and the estimate remains highly uncertain.\"},{\"question\":\"How does the proposed rigidity-based approach improve UAV trajectory optimization?\",\"answer\":\"It maximizes the smallest nonzero singular value of the rigidity matrix for the UAV–target sensing graph to improve geometric conditioning and reduce position ambiguity, and it uses a pruning-based reduction strategy for efficient real-time 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is rapid emergency target localization considered critical in public-safety networks?","Question",{"text":75,"@type":76},"Reliable and timely positioning directly affects safety-of-life services and mission outcomes, and even small delays in obtaining a dependable location can have severe consequences.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What limitation affects existing FIM-based UAV trajectory optimization methods?",{"text":80,"@type":76},"They depend on the current target estimate and can perform poorly in the early mission stage when measurement diversity is limited and the estimate remains highly uncertain.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed rigidity-based approach improve UAV trajectory optimization?",{"text":84,"@type":76},"It maximizes the smallest nonzero singular value of the rigidity matrix for the UAV–target sensing graph to improve geometric conditioning and reduce position ambiguity, and it uses a pruning-based reduction 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