Determining the coordinates of unmanned aerial vehicles
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Summary
This paper addresses the challenge of accurately determining the coordinates, trajectories, and spatial movement parameters of unmanned aerial vehicles (UAVs), particularly in military contexts where detecting enemy reconnaissance drones is critical. The authors identify a significant limitation in existing anti-aircraft defense systems: while they can neutralize aircraft, they require precise coordinate data (within roughly ten meters) that is difficult to obtain for UAVs using modern radar communication methods. Traditional passive radar techniques, such as the difference-range method, are noted to be ineffective for UAVs flying without radio frequency control signals and require extensive technical infrastructure and personnel. To overcome these limitations, the study aims to develop a method for UAV coordinate determination using kinematic projection, a theoretical approach derived from descriptive geometry. The methodology combines classical orthogonal design with the dynamic features of kinematic design to graphically display and calculate the spatial movements of objects. The researchers utilized physical and mathematical modeling of fast-moving processes, along with mathematical statistics for result analysis. Graphical models were created using AutoCAD software. The core of the proposed method involves a setup with two radar location stations (RLS) separated by a distance of 500–1,000 meters and a command post. These stations define a base plane, while a parallel "picture plane" is established at a height greater than the UAV’s approximate flight altitude. The system records the angles of inclination of electromagnetic projecting beams from the RLSs to the UAV relative to the base plane and the line connecting the stations. The results demonstrate that the UAV’s instantaneous location can be unambiguously determined by calculating the intersection of these projecting beams with the picture plane using analytical or graphical methods. The authors provide specific mathematical dependencies derived from stereometry and analytical geometry to calculate the UAV’s Cartesian coordinates (X, Y, Z). An example calculation shows that for RLS stations 500 meters apart and specific beam inclination angles, the method successfully calculates the UAV’s position, including its distance from each station and its flight height. The calculations proved sensitive to changes in initial conditions, such as the angle of inclination, confirming the logical validity of the mathematical model. The study concludes that the kinematic design method is more accurate than existing radar search methods because it is not sensitive to extraneous false signals reflected from terrain, trees, or buildings. This insensitivity to background noise allows for precise coordinate determination even for UAVs that are "invisible" to traditional direction finders. Furthermore, the authors note that the method’s simplicity and reliance on basic geometric principles make it suitable for implementation in compatible radar software. Beyond military applications, the technique holds potential for civilian uses, including construction, aerial surveying, automated land processing, and underwater object detection.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-19 |
| archive | success | canonical_url | — | — | 1 | 2026-06-26 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-20 |
| chunk | success | chunk | — | — | 1 | 2026-06-20 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-20 |
| promote | success | — | — | — | 1 | 2026-06-19 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-20 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
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