Using an Accelerometer Configuration to Improve the Performance of a MEMS IMU: Feasibility Study with a Pedestrian Navigation Application
Bibliographic record
Abstract
This paper proposes a new approach to improve the performance of a MEMS IMU namely the use of an aiding Gyroscope-Free IMU (GFIMU); a configuration of accelerometers capable of determining the motion of a rigid body. While a GFIMU is theoretically capable of replacing a traditional strapdown IMU, there are several practical issues that make the approach less that ideal. The combination of GFIMU with a gyroscope that is used in this paper is referred to as a GFIMU+. A prototype GFIMU+ was constructed by rigidly attaching five MEMS IMUS to a compact, purposedesigned plastic block. Measurements from all five triaxial accelerometers are combined with those of a single triaxial gyroscope in a novel Extended Kalman Filter (EKF). The states of the EKF comprise the angular velocity and the biases of the triaxial gyroscope and five triaxial accelerometers. The estimates from the EKF are used to form the inputs for a GPS/INS tight-integration so that the performance of the GFIMU+ and a traditional MEMS IMU can be compared at two levels; first the errors in the angular velocity and specific force estimates, and then in the position domain. Pedestrian data was collected by mounting the GFIMU+ and a tactical grade reference IMU and GPS antenna to a rigid backpack. Various routes were walked around the University of Calgary campus. It was found that in this particular application, the GFIMU+, while performing much better than the GFIMU as predicted, allowed only marginal gains over a traditional MEMS IMU using sensors of the same grade. This is likely due to the low angular dynamics in the application.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".