Performance evaluation of time compression overlap-add radar systems based on order-statistics CFAR under convolution noise jamming
Bibliographic record
Abstract
We introduce a new scheme that integrates the Time Compression OverLap-Add (TC-OLA) spread spectrum technique into radar systems, more specifically the Linear Frequency Modulation Pulse Compression (LFM-PC) radar. This technique increases the signal to noise ratio (SNR) and, as a consequence, enables a greater processing gain compared to the traditional radar LFM-PC systems. In addition, TC-OLA allows the radar designer to control the spreading of the signal and therefore provides a better immunity against powerful jamming techniques. In our simulation, we extend the conventional LFM-PC radar model by appropriately adding Time Compression (TC) and Overlap-add (OLA) blocks at the transmitter and receiver, respectively. The evaluation performance of the proposed system and the convention LFM are done under AWGN and under one of the smart jamming technique called Convolution Noise Jamming (CNJ) using different Constant False Alarm Rate (CFAR) algorithms, namely, Cell-Average (CA), Greatest-Of (GO), and Order-Statistics (OS) CFAR. Using the TC-OLA-based LFM radar system, we show that we have higher SNRs while preserving the same Doppler shift and target time delay as the conventional LFM radar system. Furthermore, the proposed radar model relies on high sample rates only after the conventional LFM radar transmitter blocks and before conventional LFM radar receiver blocks. Therefore, it does not require changing any parameters of the conventional radar blocks.
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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 | 0.003 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".