Localization of Indoor Mobile Robot Using Minimum Variance Unbiased FIR Filter
Why this work is in the frame
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Bibliographic record
Abstract
The demand of indoor localization has recently grown quickly in industries. In general, a localization system is required to be reliable, fast, and have high accuracy. In this paper, the ultrawideband (UWB) technique is combined with the inertial navigation sensor (INS) to form a coupled UWB/INS localization framework, which inherits the advantages from both components. A minimum variance unbiased finite impulse response (MVU FIR) method is then applied to obtain accurate position and velocity estimations from noisy measurements. Two experiments and several simulations are conducted. Compared with the traditional Kalman filter (KF) and particle filter, the MVU FIR filter exhibits better immunity to the errors about a priori knowledge of noise variances. It can handle the kidnapped problem, and recover from some extreme failures satisfactorily. Moreover, the MVU FIR filtering algorithm is fast and easily implementable. Its online computational time is even lower than that of the KF, which is favorable in localization applications.
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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 | 0.001 |
| 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