Radar and radio data fusion platform for future intelligent transportation system
Bibliographic record
Abstract
We present a software-defined data fusion system which integrates both radar (sensing) function and radio (communication) function within a single transceiver platform. In the proposed architecture, the radar mode and the radio mode operate in different time slots. The required modulated waveform is generated with the help of a direct digital synthesizer (DDS) that is able to control signal parameters such as amplitude, frequency and phase with very high resolution. For the radar mode, a specially arranged trapezoidal frequency modulation continuous-wave (TFMCW) modulation scheme is adopted, which combines three time intervals, namely an up-chirp, a constant-frequency period and a down-chirp. As such, range-velocity ambiguity can be resolved. Moreover, a constant-frequency period follows the radar cycle in the transmitted signal, which can be encoded with information data using different modulation schemes such as ASK, FSK, PSK, and some combinations among them. A low-frequency prototype for the 5.9-GHz dedicated short range communication (DSRC) system was designed and prototyped. Both system simulation results and preliminary measurement results have proved the proposed concept. The presented system has demonstrated such advantages as low cost, low complexity, and versatile functionality, which promises to play an important role in the design of future intelligent transportation system.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".