Navigating Signal Outages: Radar-Augmented 5G mmWave Wireless Positioning for Land Vehicles
Bibliographic record
Abstract
High-precision positioning in areas where Global Navigation Satellite Systems (GNSS) are degraded or unavailable is a necessity for the autonomous vehicles (AVs) of today and the near future and remains an active research problem. Fifth-generation (5G) millimeter-wave (mmWave) technology presents a promising answer to wireless-based positioning in GNSS-denied environments. Like GNSS however, 5G positioning systems are expected to encounter brief signal outages in real, dynamic driving environments. During these outages, the positioning system must maintain its accuracy until a signal is available once more by relying on alternate technologies. On-board motion sensors (OBMS) including inertial measurement units (IMU)s and odometers are a logical solution to this problem, maintaining a position estimate through dead-reckoning methods. A classic solution is the integration of an odometer, or wheel encoder, with measurements from an IMU. Wheel encoders are limited by a fixed resolution and a relatively low data rate. Electronic Scanning Radar (ESR) are low-cost sensors found on most modern vehicles and measure the range, angle, and Doppler velocity of targets in their environment. In this paper, we explore the use of an ESR for forward velocity estimation as an alternative to the wheel encoder. ESR-based velocity estimation is integrated with 5G positioning, and its ability to maintain high positioning accuracy during 5G signal outages is assessed. Overall, due to an increased resolution and data rate, ESR velocity estimates were found to sustain a higher positioning accuracy during signal outages when compared to wheel-based odometry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".