Inertial sensors technologies for navigation applications: state of the art and future trends
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Inertial navigation represents a unique method of navigation, in which there is no dependency on external sources of information. As opposed to other position fixing navigation techniques, inertial navigation performs the navigation in a relative sense with respect to the initial navigation state of the moving platform. Hence, inertial navigation systems are not prone to jamming, or spoofing. Inertial navigation systems have developed vastly, from their occurrence in the 1940s up to date. The accuracy of the inertial sensors has improved over time, making inertial sensors sufficient in terms of size, weight, cost, and accuracy for navigation and guidance applications. Within the past few years, inertial sensors have developed from being purely mechanical into incorporating various technologies and taking advantage of numerous physical phenomena, from which the dynamic forces exerted on a moving body could be computed accurately. Besides, the evolution of inertial navigation scheme involved the evolution from stable-platform inertial navigation system, which were mechanically complicated, to computationally demanding strap-down inertial navigation systems. Optical sensory technologies have provided highly accurate inertial sensors, at smaller sizes. Besides, the vibratory inertial navigation technologies enabled the production of Micro-electro-machined inertial sensors that are extremely low-cost, and offer extremely low size, weight and power consumption, making them suitable for a wide range of day-to-day navigation applications. Recently, advanced inertial sensor technologies have been introduced to the industry such as nuclear magnetic resonance technology, cold-atom technology, and the re-introduction of fluid-based inertial sensors. On another note, inertial sensor errors constitute a huge research aspect in which it is intended for inertial sensors to reach level in which they could operate for substantially long operation times in the absence of updates from aiding sensors, which would be a huge leap. Inertial sensors error modeling techniques have been developing rapidly trying to ensure higher levels of navigation accuracy using lower-cost inertial sensors. In this review, the inertial sensor technologies are covered extensively, along the future trends in the inertial sensors’ technologies. Besides, this review covers a brief overview on the inertial error modeling techniques used to enhance the performance of low-cost sensors.
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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 | 0.001 |
| 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 it