A Novel Smeared Synthesized LFM TC-OLA Radar System: Design and Performance Evaluation
Bibliographic record
Abstract
This paper introduces a novel smeared synthesized LFM (SSLFM) time compression overlap-add (TC-OLA) radar system. The new system allows us to control the signal to noise ratio level, and, therefore, obtain a higher processing gain compared to the traditional LFM-PC radar systems. In addition, it allows us to control the signal spectrum spreading, making it more immune to noise jamming. The new SSLFM signal is obtained by either multiplying the LFM waveform with a complex unit signal with the random phase or by encoding the time compression signal with the random phase at the transmitter. A denoising processor, placed either before or after the OLA processor, is used to remove the random phase from the SSLFM and forward the resulted LFM signal to the rest of the conventional LFM-PC radar receiver system. The new SSLFM TC-OLA radar system enjoys a better low probability of intercept feature while maintaining the LFM time sidelobe and Doppler tolerance properties. Moreover, the additional modules in the new radar system do not require changing the core LFM radar components. Using TC-OLA and denoising requires a synchronization system (SS) to properly recover the LFM signal. We, therefore, offer three SSs. The performance evaluation of the new radar system shows its superiority over the traditional LFM, the wideband LFM, and the TC-OLA-based LFM radars, especially under powerful noise jamming. The synchronization system is implemented and tested experimentally using software-defined radar.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".