Delay and Doppler shift estimation for OFDM-based radar-radio (RadCom) system
Bibliographic record
Abstract
The joint operation of radar sensing and wireless communication, namely RadCom system, yields a unique platform to meet the requirements of future intelligent transportation networks. Taking advantages of OFDM waveform, the range-Doppler coupling issues can be overcome for radar applications and complex equalization filter is no longer necessarily used to cope with frequency-selective fading channel because of multi-path. This paper presents a technique for simultaneous estimation of the range and Doppler shift of targets using an OFDM-based Radcom system. Unlike the previous method based on fourier analysis to estimate the range and Doppler shift, we derive a subspace-based algorithm by applying a smoothing approach for joint estimation of range and Doppler shift without the pair matching problem. Compared to the previous method, our method is able to at least exhibit the following three advantages: 1) higher resolution for multiple targets, 2) less time-based data, and 3) avoidance of pair-matching techniques. Therefore, this method is more suitable for OFDM-based RadCom systems with high mobility and high data rate. Furthermore, the proposed algorithm is compared with the current counterpart with computer simulations.
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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 | 0.000 | 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) | 0.001 | 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".