A vector-based gyro-free inertial navigation system by integrating existing accelerometer network in a passenger vehicle
Bibliographic record
Abstract
Modern automotive electronic control and safety systems, including air-bags, anti-lock brakes, anti-skid systems, adaptive suspension, and yaw control, rely extensively on inertial sensors. Currently, each of these sub-systems uses its own set of sensors, the majority of which are low-cost accelerometers. Recent developments in MEMS accelerometers have increased the performance limits of mass-produced accelerometers far beyond traditional automotive requirements; this growth trend in performance will soon allow the implementation of a gyro-free inertial navigation system (GF-INS) in an automobile, utilizing its existing accelerometer network. We propose, in addition to short-term aid to GPS navigation, a GF-INS can also serve in lieu of more expensive and less reliable angular rate gyros in vehicle moment controls and inclinometers in anti-theft systems. This work presents a modified generalized GF-INS algorithm based on four or more vector (triaxial) accelerometers. Historically, GF-INS techniques require strategically-placed accelerometers for a stable solution, hence inhibiting practical implementations; the vector-based GF-INS allows much more flexible system configurations and is more computationally efficient. An advanced attitude estimation technique is presented, utilizing coupled angular velocity terms that emerged as a result of the intrinsic misalignment of real vector accelerometers; this technique is void of singularity problems encountered by many prior researchers and is particularly useful when error due to the integration of angular accelerations is prominent, such as in low-speed systems or long-duration navigations. Furthermore, an initial calibration method for the vector-based GF-INS is presented. In the experimental setup, four vector accelerometers, based on Analog Devices accelerometers, are assembled into a portable, one cubic-foot, rigid structure, and the data is compared with that of a precision optical position tracking system. Finally, the feasibility of a GF-INS implementation in an automobile is assessed based on experimental results.
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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 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".