Optimum coherent integration time for a surface target with periodic acceleration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper we consider the effect of surface target dynamics on the detection performance of coherent radar. The echo from a surface target traveling with a constant velocity, that is not radial to the radar look direction or constant rate acceleration is represented in the form of chirp function. This is a simplistic model that must be extended to account for other periodic accelerations acting on the target. The dominant periodic acceleration is the radial surge component that may result in the smearing of the targets Doppler. This radial surge component is a result of the interaction of the target with the ocean wave. This effect must be taken into account when determining the optimum coherent integration time (CIT). In this paper we extend the model to accommodate an echo from a periodic accelerating target with a frequency-modulated signal that represents the surge component. The optimum CIT is derived based on maximizing the signal-to-noise ratio (SNR) at the matched filter output. The results are verified based on data collected from a high frequency surface wave radar (HFSWR) operating at 15 MHz.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it