On Time Compression Overlap-Add Technique in Linear Frequency Modulation Pulse Compression Radar Systems: Design and Performance Evaluation
Bibliographic record
Abstract
This paper introduces a novel approach to incorporate the time compression overlap-add (TC-OLA) technique used in communication systems into linear frequency modulation pulse compression (LFM-PC) radar systems. This technique significantly boosts the signal-to-noise ratio (SNR) and provides a robust processing gain compared with the traditional radar LFM-PC systems. In addition, TC-OLA provides a better immunity against powerful jamming techniques. At the transmitter side, we divide a digitized LFM chirp signal into a controlled number of overlapping segments. We then speed up each segment by increasing the sampling rate to account for the segment overlap. At the receiver side, we apply OLA to reconstruct the signal with a much higher gain. To simulate and evaluate the performance of the new system, we extend the conventional LFM-PC radar model, which includes matched filter (MF) processor, moving target detector (MTD), and two common constant false alarm rate (CFAR) algorithms, by suitably adding TC and OLA blocks at the transmitter and receiver, respectively. Using the TC-OLA-based LFM radar system, we have control over the SNR level and the spectrum spread while preserving the same Doppler shift and target time delay as the conventional LFM radar system. Furthermore, we transform LFM chirp signal into a novel TC signal that inherits LFM properties while possessing better immunity to jamming. Moreover, the proposed radar model relies on high sample rates only when needed and, therefore, does not require changing MF, MTD, and CFAR as is the case for a wideband LFM radar with the same processing gain. Detailed comparisons between the conventional LFM and the wideband LFM radars against the proposed model are also presented.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".