Flight Test Performance of an RFSoC Based Direct RF FMCW Radio Altimeter
Bibliographic record
Abstract
The radar altimeter (RA) is one of the most important sensors in avionics systems. It is the only sensor that reliably indicates altitude above the ground. With the roll-out of the 5G network, many concerns have been raised about the interference between the 5G network and radar altimeters. This issue highlights the importance of future-proofing avionics systems. One way to do this is by leveraging software-defined radio (SDR) technology. Advances in high-speed analogue-to-digital converters (ADCs) and digital-to-analogue converters (DACs) have made it possible to directly digitise the radio frequency (RF) signal and get rid of the RF mixing stage. This is called Direct RF Sampling (DRFS). This work aims to demonstrate the feasibility, capability, and performance of a DRFS radar altimeter based on the Xilinx RFSoC technology. The radar altimeter was tested in a laboratory environment and in a flight test. The laboratory test results show that the radar altimeter meets the MOPS tolerances. The flight test results show that the prototype is accurate at altitudes above <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{5 0 0} \mathbf{f t}$</tex> but there are still some issues that need to be addressed. The results show that DRFS is a viable solution for high bandwidth systems like the radar altimeter.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".