Indoor Positioning with Sensors in a Smartphone and a Fabricated High-Precision Gyroscope

Chen, Dianzhong; Zhang, Wenbin; Zhang, Zhongzhao

doi:10.1007/978-981-13-6504-1_134

Cited by 1 publication

(1 citation statement)

References 16 publications

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“…Heading direction estimation is also an important factor in PDR and determines the direction of the entire track deflection [ 48 ]. The measured angular velocity of gyroscope sensors

, the angular velocity of earth coordinate system relative to inertial coordinate system

, the angular velocity of navigation coordinate system relative to earth coordinate system

and the angular velocity of body coordinate system relative to navigation coordinate system

satisfy as follows:

where

is the transfer matrix between earth coordinate system and navigation coordinate system.…”

Section: Methodsmentioning

confidence: 99%

A Positioning and Navigation Method Combining Multimotion Features Dead Reckoning with Acoustic Localization

Yan,

Xu,

Luo

et al. 2023

Sensors

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Accurate location information can offer huge commercial and social value and has become a key research topic. Acoustic-based positioning has high positioning accuracy, although some anomalies that affect the positioning performance arise. Inertia-assisted positioning has excellent autonomous characteristics, but its localization errors accumulate over time. To address these issues, we propose a novel positioning navigation system that integrates acoustic estimation and dead reckoning with a novel step-length model. First, the features that include acceleration peak-to-valley amplitude difference, walk frequency, variance of acceleration, mean acceleration, peak median, and valley median are extracted from the collected motion data. The previous three steps and the maximum and minimum values of the acceleration measurement at the current step are extracted to predict step length. Then, the LASSO regularization spatial constraint under the extracted features optimizes and solves for the accurate step length. The acoustic estimation is determined by a hybrid CHAN–Taylor algorithm. Finally, the location is determined using an extended Kalman filter (EKF) merged with the improved pedestrian dead reckoning (PDR) estimation and acoustic estimation. We conducted some comparative experiments in two different scenarios using two heterogeneous devices. The experimental results show that the proposed fusion positioning navigation method achieves 8~56.28 cm localization accuracy. The proposed method can significantly migrate the cumulative error of PDR and high-robustness localization under different experimental conditions.

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“…Heading direction estimation is also an important factor in PDR and determines the direction of the entire track deflection [ 48 ]. The measured angular velocity of gyroscope sensors

, the angular velocity of earth coordinate system relative to inertial coordinate system

, the angular velocity of navigation coordinate system relative to earth coordinate system

and the angular velocity of body coordinate system relative to navigation coordinate system

satisfy as follows:

where

is the transfer matrix between earth coordinate system and navigation coordinate system.…”

Section: Methodsmentioning

confidence: 99%

A Positioning and Navigation Method Combining Multimotion Features Dead Reckoning with Acoustic Localization

Yan,

Xu,

Luo

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

Indoor Positioning with Sensors in a Smartphone and a Fabricated High-Precision Gyroscope

Cited by 1 publication

References 16 publications

A Positioning and Navigation Method Combining Multimotion Features Dead Reckoning with Acoustic Localization

A Positioning and Navigation Method Combining Multimotion Features Dead Reckoning with Acoustic Localization

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