Multi-IMU and mmWave Radar Integration for Robust Land Vehicle Navigation
Bibliographic record
Abstract
Despite the crucial role of the Global Navigation Satellite System (GNSS) for land vehicle navigation, it is prone to wireless signal degradation challenges that can compromise positioning accuracy and availability. Integrating GNSS with Dead Reckoning (DR) systems like the Inertial Navigation System (INS) offers a solution. Still, it is hindered by drifting errors and the limitations of Micro-Electro-Mechanical Systems (MEMS) technology. This paper aims to address the challenges faced by land vehicle navigation systems, particularly during extended GNSS outages, by leveraging the synergistic integration of data from multiple redundant Inertial Measurement Units (IMUs) and mmWave radar technology. To be specific, a Kalman Filter framework is employed to fuse raw measurements from multiple homogeneous IMUs, yielding refined and robust measurements. An Autoencoder model calibrates and denoises raw radar measurements, ensuring accurate and reliable forward velocity estimates. The enhanced measurements are then processed by a mechanization algorithm, yielding a navigation solution with significantly reduced drifting errors. The effectiveness of this approach has been validated with real-world data collected from IMUs and radar during road tests in downtown Kingston, Ontario, Canada. Our comprehensive testing across various scenarios has shown that the proposed method significantly improves navigation performance, achieving average heading and position accuracy enhancements of 89% and 62%, respectively.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".