[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-81804-en":3,"doc-seo-81804-105":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},81804,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Field Deployable RF Capture System for Indoor Outdoor and Foliage Environments","Reliable and reproducible RF measurements in real-world settings are necessary to characterize spectrum behavior across ISM/WiFi bands, licensed mid-band allocations, and next-generation wireless deployments. This paper designs, implements, and evaluates a compact battery-powered capture platform integrating a HackRF One SDR, Raspberry Pi 5, embedded GNSS, regulated battery power, and high-speed storage. It records continuous, SigMF-compliant IQ data with per-segment geolocation and timestamps, enabling reproducible, context-aware analysis. Field results at 2.45 GHz show environment-consistent propagation signatures for foliage, urban outdoor, and indoor office use cases.","Field-Deployable RF Capture System for Indoor, Outdoor, and Foliage Environments  \nLawrence Obiuwevwi, Krzysztof J. Rechowicz, Vikas Ashok, Sachin Shetty, Peter B. Foytik, Jared Cochran, Jeff Bobrow, and Sampath Jayarathna  \nOld Dominion University, Norfolk, VA, USA  \n[lobiu001@odu.edu](lobiu001@odu.edu), [krechowi@odu.edu](krechowi@odu.edu), [vganjiqu@cs.odu.edu](vganjiqu@cs.odu.edu),  \n[sshetty@odu.edu](sshetty@odu.edu), [pfoytik@odu.edu](pfoytik@odu.edu), [jcochran@odu.edu](jcochran@odu.edu),  \n[jbobrow@odu.edu](jbobrow@odu.edu), [sampath@cs.odu.edu](sampath@cs.odu.edu)  \narXiv :2607 .0 1368v 1 [ cs .AR] 1 Jul 2026  \nAbstract—Reliable and reproducible radio-frequency (RF) measurements in real-world environments are essential for characterizing spectrum behavior across unlicensed ISM and WiFi bands, licensed mid-band allocations, and emerging nextgeneration wireless deployments. Existing measurement platforms are often laboratory-grade, cost-prohibitive, or dependent on fixed infrastructure, limiting their practicality for rapid, distributed, or long-duration field campaigns. This paper presents the design, implementation, and field evaluation of a compact, battery-powered RF capture system integrating a HackRF One software-defined radio, Raspberry Pi 5 single-board computer, embedded GNSS receiver, regulated battery power system, and high-speed solid-state storage. The platform records continuous IQ data at up to 20 Msps in SigMF-compliant format, embedding per-segment geolocation and temporal metadata to enable reproducible, context-aware spectrum analysis. Field experiments conducted at 2.45 GHz across three representative environments, dense foliage, urban outdoor, and indoor office, reveal distinct and environment-consistent propagation signatures. Foliage environments exhibit near-noise-floor power levels between −76 and −82 dBFS with minimal spectral structure, consistent with canopy-induced attenuation on the order of 30 dB. Urban deployments produce rich multipath activity spanning a 30 dB dynamic range with multiple overlapping WiFi channel envelopes and frequent ISM-band interference transients. Indoor environments show strong WiFi channel dominance with an estimated 20–25 dB building entry loss relative to outdoor conditions and an elevated spectral interference floor of 8– 10 dB driven by structural multipath reflections. The system sustained 75–85 MB/s write throughput across all sessions with no dropped samples, no buffer underruns, and sub-second GNSS synchronization providing meter-level positional accuracy. These results demonstrate that a cost-effective, portable SDR platform can produce high-fidelity, geotagged IQ datasets suitable for initial spectrum characterization, interference analysis, radio  \nenvironment mapping, and environment-aware wireless research. Index Terms—RF Capture, 5G, spectrum, GNSS, WiFi, SDR.  \nI. INTRODUCTION  \nCapturing radio-frequency (RF) data in real-world environments is critical for characterizing spectrum usage, interference, and propagation behavior across licensed and unlicensed bands. These include the heavily utilized 2.4 GHz ISM and WiFi bands, as well as emerging mid-band allocations such as the 3.45–3.55 GHz range recently auctioned by the Federal Communications Commission for next-generation wire-  \nless services [1], [2] . Unlike controlled laboratory conditions, field environments introduce complex and highly variable propagation effects, foliage-induced attenuation [3]–[5], urban multipath scattering [6]–[8], and indoor penetration loss [9],[10], that fundamentally shape how wireless systems behave in practice. Understanding these effects requires empirical, in-situ measurements collected under realistic deployment conditions.  \nDespite growing interest in environment-aware wireless systems and data-driven spectrum analysis, the infrastructure for collecting high-quality field measurements remains limited. Many professional RF measurement instruments, including ve","cbCaihQKoevEnPab","https://ap.wps.com/l/cbCaihQKoevEnPab","pdf",2289744,4,1,9,"English","en",105,"# Introduction\n## Motivation and gaps in current field measurement\n## Related standards and environment-aware mapping\n## Proposed field-deployable RF capture system","[{\"question\":\"Why are real-world RF measurements needed for spectrum and interference analysis?\",\"answer\":\"Real environments introduce highly variable propagation effects—such as foliage attenuation, urban multipath, and indoor penetration loss—that shape system behavior beyond what laboratory conditions capture.\"},{\"question\":\"What integrated components make up the proposed field-deployable RF capture system?\",\"answer\":\"The system combines a HackRF One SDR, a Raspberry Pi 5 single-board computer, an embedded GNSS receiver, a regulated battery power system, and high-speed solid-state storage in a single autonomous enclosure.\"},{\"question\":\"How does the system ensure geolocation-aware and reproducible dataset generation?\",\"answer\":\"It records continuous IQ data in a SigMF-compliant format while automatically embedding per-segment geographic coordinates and timestamps, enabling context-aware spectrum 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are real-world RF measurements needed for spectrum and interference analysis?","Question",{"text":75,"@type":76},"Real environments introduce highly variable propagation effects—such as foliage attenuation, urban multipath, and indoor penetration loss—that shape system behavior beyond what laboratory conditions capture.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What integrated components make up the proposed field-deployable RF capture system?",{"text":80,"@type":76},"The system combines a HackRF One SDR, a Raspberry Pi 5 single-board computer, an embedded GNSS receiver, a regulated battery power system, and high-speed solid-state storage in a single autonomous enclosure.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the system ensure geolocation-aware and reproducible dataset generation?",{"text":84,"@type":76},"It records continuous IQ data in a SigMF-compliant format while automatically embedding per-segment geographic coordinates and timestamps, enabling 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