High-resolution hybrid TDM-CDM MIMO automotive radar
Bibliographic record
Abstract
This paper proposes a deep learning (DL)-based high-resolution hybrid time-division multiplexing (TDM) and code-division multiplexing (CDM) multiple input multiple output (MIMO) automotive radar to enhance the discrimination capabilities of the radar in a cluttered environment. The hybrid TDM-CDM approach is implemented by partitioning the transmit and receive arrays into subarrays, applying CDM across the subarrays, while TDM is used within each subarray. On the other hand, the DL-based scheme utilizes the SqueezeNet deep convolutional neural network (DCNN), which treats the angle, range, and Doppler estimations of the extracted targets as a multi-label classification problem. Compared to CDM-MIMO radars, this approach requires fewer spreading codes, alleviating the challenge of spreading and despreading over each element. Compared to TDM-MIMO radars, it requires fewer time slots, increasing the refresh rate. Our approach outperforms existing DL-based TDM-MIMO radar systems and performs similarly to DL-based CDM-MIMO radar systems but with reduced complexity. Simulation results show that an angular resolution of 0.25° was achieved using 12-element transmit and receive arrays, each partitioned into three subarrays.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".