Joint range and velocity estimation for integration of radar and communication based on multi‐symbol OFDM radar pulses
Bibliographic record
Abstract
Abstract Integration of radar and communication (IRC) is conducive to improving hardware resource utilization and spectrum utilization. This paper proposes a joint range and velocity estimation method for IRC based on multi‐symbol orthogonal frequency division multiplexing (OFDM) radar pulses. A two‐step parameters estimation method is applied, where estimating the signal parameters via rotational invariance technique (ESPRIT) and weighted subspace fitting (WSF) perform rough and fine estimations. We first build a receiving model for integrated signal based on element‐wise division. The ESPRIT method is then adopted based on the double Vandermonde structure array manifold for a rough estimation. It has relatively high estimation error but low computational complexity. The WSF method based on alternating projection (AP) algorithm is introduced to make the estimation more precise and reduce its error. It uses the estimation results in the first step and reduces the overall computational cost. Simulation results show that the proposed method reduces the computational complexity while improving the estimation accuracy compared with the WSF method.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".