United States Department of Transportation
i
Ohio Department of Transportation District Highway Maintenance Research On-Call Task 07: Radar Emplacement Prediction Tool Exploration & Recommendations
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2023-08-01
[PDF-1.75 MB]
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Edition:Final Report
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Abstract:Emerging autonomous aircraft require unique infrastructure to provide vital information to ensure they remain well-clear of other flying objects. While there are many systems designed to provide such functionality, termed detect and avoid (DAA), ground-based radars have emerged as one of the most promising technical solutions to this challenging problem. Ground-based radars can detect and communicate the location of cooperative and noncooperative aircraft or objects present in the National Airspace System (NAS) to support the safe operations of crewed and uncrewed aircraft operating in the NAS simultaneously. Ground-based radars do not require aircraft to carry heavy equipment onboard. One major drawback to ground-based radar is that their performance is heavily influenced by where the sensors are located. Nearby objects such as trees/roads/buildings generate false tracks or shield the radars returns leading to large, uncovered regions. This line-of-sight limitation is a major hurdle for emplacing such systems over a wide geographical area and makes each emplacement assessment unique. Emplacing radars strategically is necessary to maximize airspace coverage while also identifying airspace regions that are not covered. This report explores the usage of a simplified version of a software prediction tool to predict radar coverage performance in an urban environment. Physics based prediction capabilities were deemed essential to successfully utilize radar prediction models when strategically analyzing emplacement options and vendor candidates. Insights of additional criteria to consider for future software prediction models were captured along with recommended questions to ask when vetting potential radar candidates. This research focuses on understanding how to utilize software prediction models to support the rapid emplacement of radars
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