High-Accuracy Radar Parameter Estimation Under Low SNR Environments
Bibliographic record
Abstract
This paper proposes a high-accuracy radar parameter estimation method for extremely low signal-to-noise ratio (SNR) environments, integrating deep learning and connected component analysis (CCA). Accurate estimation of parameters such as time of arrival, pulse width, pulse repetition interval, bandwidth, and carrier frequency is critical for various applications, but remains challenging under high noise conditions. The proposed method consists of three key steps. First, frequency-domain pulse detection isolates radar signals from noise-contaminated samples. Second, time-frequency analysis using short-time Fourier transform is followed by UNet-based denoising and CCA to suppress noise and enhance signal features. Finally, edge-based parameter computation is applied to extract signal boundaries and estimate parameters precisely. Simulation results show that the method achieves approximately 3 dB improvement in time-domain estimation accuracy and lower root mean square error in frequency-domain parameters compared to conventional methods, across SNR levels from –20 dB to 10 dB. Test-bed experiments using Universal Software Radio Peripheral and GNURadio further validate its robustness, especially at –5 dB SNR. These findings confirm the method’s practical viability for reliable radar signal processing in challenging low-SNR environments.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".