Robust Heading Estimation for Indoor Pedestrian Navigation Using Unconstrained Smartphones

Deng, Zhongliang; Liu, Xin; Qu, Zhiyu; Hou, Changbo; Si, Weijian

doi:10.1155/2018/5607036

Cited by 13 publications

(11 citation statements)

References 32 publications

(39 reference statements)

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“…• Deng [43], detects the three motion states (random hand movements, carrying position transitions, and user turns,) timely and discriminate them accurately by a position classifier, estimates walking heading with respect to motion state.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

“…The pedestrians may put their smartphone in bag, make a phone call, or even swing in hand during walking. To solve this problem, references [39]- [43] proposed smartphone mode or posture recognition-based PDR method that calculated pedestrian heading by selecting heading model with corresponding to smartphone mode. However, the limited mode classification cannot reflect the diversity of smartphone carrying modes.…”

Section: Introductionmentioning

confidence: 99%

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Pedestrian Heading Estimation Based on Spatial Transformer Networks and Hierarchical LSTM

Wang

Luo

et al. 2019

IEEE Access

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Accurate heading estimation is the foundation of numerous applications, including augmented reality, pedestrian dead reckoning, and human-computer interactions. While magnetometer is a key source of heading information, the poor accuracy of consumer-grade hardware coupled with the pervasive magnetic disturbances makes accurate heading estimation a challenging issue. Heading error is one of the main error sources of pedestrian dead reckoning. To reduce the heading error and enhance robustness, we proposed a novel heading estimation method based on Spatial Transformer Networks (STNs) and Long Short-Term Memory (LSTM), termed DeepHeading, which uses sensors embedded in a smartphone without any historical training data or dedicated infrastructure. We automatically annotate heading data based on map matching, and augment heading data based on device attitude. We leverage the STNs to align the device coordinate system and the navigation coordinate system, allow an unconstrained use of smartphones. Based on the characteristics of pedestrian heading continuity, we designed a hierarchical LSTM-basedSeq2Seq model to estimate the walking heading of the pedestrian. We conducted well-designed experiments to evaluate the performance of deepheading and compared it with the state-of-the-art heading estimation algorithms. The experimental results on real-world demonstrated that deepheading outperformed the compared heading estimation algorithms and achieved promising estimation accuracy with a median heading error of 4.52 • , mean heading error of 6.07 • and heading error of 9.18 • at the confidence of 80% when a pedestrian is walking in indoor environments with magnetic field disturbances. The proposed method is high-efficiency and easy to integrate with various mobile applications. INDEX TERMS Indoor positioning, heading estimation, pedestrian dead reckoning, deep learning.

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Section: Comparison With Other Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Pedestrian Heading Estimation Based on Spatial Transformer Networks and Hierarchical LSTM

Wang

Luo

et al. 2019

IEEE Access

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“…The PCA-global acceleration (GA) method is proposed for heading estimation during a mismatch condition in [34] by assuming that the least varying acceleration axis during a stride is perpendicular to the walking direction. In addition, heading estimation using PCA is proposed in [35], [36] by introducing several coordinate systems: user, device, reference which is an initially aligned plane, and global.…”

Section: Related Workmentioning

confidence: 99%

Robust Pedestrian Dead Reckoning for Multiple Poses in Smartphones

2021

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In this paper, an integration approach (IA) and parametric approach (PA) fusion-based pedestrian dead reckoning (PDR) using low-cost inertial sensors is proposed to improve the indoor position estimation. When moving indoors using a smartphone, various poses such as texting and putting a pocket are possible. In the existing PA-based positioning algorithms, it can be used only when the pedestrian's walking direction and the device's direction coincide. In order to solve the problem, this paper proposes an algorithm that constructs state variables based on the IA and uses the position vector from the PA as a measurement. If the walking direction and the device heading do not match based on the poses, the position is updated according to the direction calculated using principal component analysis (PCA) and the step length obtained from the PA. Through experiments considering various operating conditions and paths, it shows that the proposed method stably estimates the position and improves performance even in various indoor environments with the accuracy of 2.13m for Xsens MTw 2.49m for Smartphone.INDEX TERMS Indoor navigation, pedestrian dead reckoning (PDR), extended Kalman filter (EKF), integration approach (IA)-parametric approach (PA) fusion method.

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“…Many of the works fuses measurements from gyroscope and digital compass on smartphone using the KF to obtain optimized direction information [31,32,33]. For some works, Principal Component Analysis (PCA) is used to infer an accurate pedestrian heading through sensing information measured by gyroscope and accelerometer [34,35,36]. Kang and Han [37] and Kang et al [38] integrate measurements from IMU sensors based on weighted models.…”

Section: Localization Algorithmmentioning

confidence: 99%

Pedestrian Positioning Using a Double-Stacked Particle Filter in Indoor Wireless Networks

Sung

Lee

Kim

2019

Sensors

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The indoor pedestrian positioning methods are affected by substantial bias and errors because of the use of cheap microelectromechanical systems (MEMS) devices (e.g., gyroscope and accelerometer) and the users’ movements. Moreover, because radio-frequency (RF) signal values are changed drastically due to multipath fading and obstruction, the performance of RF-based localization systems may deteriorate in practice. To deal with this problem, various indoor localization methods that integrate the positional information gained from received signal strength (RSS) fingerprinting scheme and the motion of the user inferred by dead reckoning (DR) approach via Bayes filters have been suggested to accomplish more accurate localization results indoors. Among the Bayes filters, while the particle filter (PF) can offer the most accurate positioning performance, it may require substantial computation time due to use of many samples (particles) for high positioning accuracy. This paper introduces a pedestrian localization scheme performed on a mobile phone that leverages the RSS fingerprint-based method, dead reckoning (DR), and improved PF called a double-stacked particle filter (DSPF) in indoor environments. As a key element of our system, the DSPF algorithm is employed to correct the position of the user by fusing noisy location data gained by the RSS fingerprinting and DR schemes. By estimating the position of the user through the proposal distribution and target distribution obtained from multiple measurements, the DSPF method can offer better localization results compared to the Kalman filtering-based methods, and it can achieve competitive localization accuracy compared with PF while offering higher computational efficiency than PF. Experimental results demonstrate that the DSPF algorithm can achieve accurate and reliable localization with higher efficiency in computational cost compared with PF in indoor environments.

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Robust Heading Estimation for Indoor Pedestrian Navigation Using Unconstrained Smartphones

Cited by 13 publications

References 32 publications

Pedestrian Heading Estimation Based on Spatial Transformer Networks and Hierarchical LSTM

Pedestrian Heading Estimation Based on Spatial Transformer Networks and Hierarchical LSTM

Robust Pedestrian Dead Reckoning for Multiple Poses in Smartphones

Pedestrian Positioning Using a Double-Stacked Particle Filter in Indoor Wireless Networks

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