Investigation of wireless tracking performance in the tunnel-like environment with particle filter
Notice bibliographique
Résumé
When the basic nonlinear filtering problem for dynamic systems is considered, in such case, the particle filter is one of the suitable methods that has perhaps become one of the most commonly used methods in recent years.Positioning or localization falls under such a nonlinear filtering problem.Positioning is a matter of interest for both domestic and industrial application, due to its potential use in wide range of context-aware services that it can enable by leveraging the Internet of Things approach.However, in areas where GPS signals are not available such as underground tunnel roadways, where the localization is done mostly using radio beacons.This study mathematically simulates tracking operation in such a tunnel-like situation and studied the position estimation by the particle filter.From the results, we were able to visualize how varying different configuration parameters affect the estimation accuracies and also get an idea of worst-case estimates by seeing its standard deviation of estimated positions for different instances of repeated experiments.And our results also confirmed that deploying additional beacons have a contribution to the improvement in error tolerance.However, the improvements are significantly notable only around the point where beacon has been added.When the basic nonlinear filtering problem for dynamic systems is considered, in such case, the particle filter is one of the suitable methods that has perhaps become one of the most commonly used methods in recent years.Positioning or localization falls under such a nonlinear filtering problem.Positioning is a matter of interest for both domestic and industrial application, due to its potential use in wide range of context-aware services that it can enable by leveraging the Internet of Things approach.However, in areas where GPS signals are not available such as underground tunnel roadways, where the localization is done mostly using radio beacons.This study mathematically simulates tracking operation in such a tunnel-like situation and studied the position estimation by the particle filter.From the results, we were able to visualize how varying different configuration parameters affect the estimation accuracies and also get an idea of worst-case estimates by seeing its standard deviation of estimated positions for different instances of repeated experiments.And our results also confirmed that deploying additional beacons have a contribution to the improvement in error tolerance.However, the improvements are significantly notable only around the point where beacon has been added.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».