Improved radar range and velocity resolution using the fast orthogonal search
Bibliographic record
Abstract
Abstract The authors show improved radar range and velocity resolution is achieved using fast orthogonal search in place of the standard fast Fourier transform. The method reliably detects targets that are close either in range or in velocity, which is relevant today given recent advances in target swarms. The method presented also allows radars to detect slower targets closer to the zero‐Doppler region, like uncrewed aerial systems (drones) moving slowly to escape detection. The authors also show a novel method for estimating the velocity spectrum of a pulse‐Doppler radar using the in‐phase and quadrature samples directly by presenting unique terms to the fast orthogonal search. This eliminates the need to convert into complex samples for processing. The authors compared the performance of the new fast orthogonal search method against the fast Fourier transform using both simulated pulse‐Doppler radar data and real frequency‐modulated continuous wave radar data taken in an anechoic chamber. The simulated pulse‐Doppler scenarios included targets closely spaced in velocity and targets near the zero‐Doppler null, all at a variety of signal‐to‐interference ratios. In all cases, the fast orthogonal search method was shown capable of detecting both the closely‐spaced and near‐zero‐Doppler targets a greater percentage of time compared to the fast Fourier transform. Furthermore, when the methods were compared using real frequency‐modulated continuous wave radar data, the stationary targets were resolved at 0.4 m using the fast orthogonal search as compared to 0.6 m for the fast Fourier transform. Thus, this new method is capable of greater radar range and velocity resolution than the fast Fourier transform.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".