The Development of A MEMS-Based Inertial/GPS System for Land-Vehicle Navigation Applications
Bibliographic record
Abstract
With the development of low-cost inertial sensors and GPS technology, MEMS-based INS/GPS navigation systems are beginning to meet the increasing demands of lower cost, smaller size, and seamless navigation solutions for land vehicles. But there are still two challenges for current MEMS navigation systems before they can be commercialized. The first one is to further reduce the cost of the systems, which is mainly governed by the cost of MEMS gyros (>$10/axis). The second is to improve the accuracy of the systems, especially during GPS signal outages. The Mobile Multi-Sensor Systems (MMSS) Research Group in the University of Calgary developed its prototype MEMS navigation system in 2004 and published preliminary results in 2005. This paper will report further progress of the systems that tried to fulfill the challenges of the current MEMS system. The system cost issue was addressed by introducing the Partial IMU (ParIMU) configuration that consists of only one heading gyro (Gz) and two horizontal accelerometers (Ax and Ay). The system cost can be reduced significantly since the hardware required for two gyros and one accelerometer is eliminated. A universal algorithm based on the concept of pseudo sensors was developed to process the ParIMU signals. Results have shown that the performance has obvious degradation but still can meet the requirements of some applications, especially with additional aiding, (such as non-holonomic constraint). On the other hand, a Backward Smoothing (BS) algorithm (Rauch-Tung-Strieber smoother) was introduced to improve the navigation performance of the MEMS navigation system. Results showed that the BS can reduce the navigation errors significantly; especially the position drifts during GPS signal outages. Of course, this BS can only be applied for post-processing scenarios. Studies in this paper have shown that the ParIMU and the BS are two measures that can well meet the challenges of current MEMS navigation systems to a large extent.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".