Spectrum monitoring with unmanned aerial vehicle carrying a receiver based on the core technology of cognitive radio – A software-defined radio design
Bibliographic record
Abstract
In this paper, we design and make a prototype of an aerial spectrum monitoring system that consists of a ground control station and a four-rotor unmanned aerial vehicle (UAV). This UAV carries a software-defined radio (SDR) receiver to perform spectrum monitoring tasks, including signal strength, frequency occupancy, and signal analysis. A light and low-cost SDR-based dongle consisting of RTL2832U chip and R820T tuner is employed as the monitoring receiver. A global positioning system and an electronic compass system are built on board to report the UAV’s position and direction. The open-source development platform GNU Radio is employed to design the radio monitoring system through the use of software-defined blocks. The proposed aerial monitoring system can detect radio signals in the frequency range of 25–1700 MHz that in practice covers the FM and DVB bands. With the prototype monitoring system, we have performed a few measurement tasks, including signal strength, waterfall display, and demodulation for identifying FM stations. Our proposed aerial monitoring system is more cost-effective than land-vehicle monitoring stations because of its much more flexible implementation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".