Learning-Based Complex Motion Patterns Recognition for Pedestrian Dead Reckoning

Luo, Yarong; Guo, Chi; Su, Jinteng; Zhang, Quan

doi:10.1109/jsen.2020.3029719

Cited by 12 publications

(3 citation statements)

References 19 publications

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“…Some authors studied how to handle the problems in PDR localization using DL architectures. The authors in [119] and [120] apply a CNN and LSTM to estimate the pedestrian motion and heading, which improves the accuracy of localization and navigation systems by giving correct motion recognition.…”

Section: Machine Learning In Pedestrian Dead Reckoningmentioning

confidence: 99%

A Survey of Machine Learning in Pedestrian Localization Systems: Applications, Open Issues and Challenges

et al. 2021

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With the popularization of machine learning (ML) techniques and the increased chipset's performance, the application of ML to pedestrian localization systems has received significant attention in the last years. Several survey papers have attempted to provide a state-of-the-art overview, but they usually limit their scope to a particular type of positioning system or technology. In addition, they are written from the point of view of ML techniques and their practice, not from the point of view of the localization system and the specific problems that ML techniques can help to solve. This article is intended to offer a comprehensive state-of-the-art survey of the ML techniques that have been adopted over the last ten years to improve the performance of pedestrian localization systems, addressing the applicability of ML techniques in this domain, along with the main localization strategies. It concludes by indicating the underlying open issues and challenges associated with the existing systems, and possible future directions in which ML techniques could improve the performance of pedestrian localization systems. Among other open issues, most previous authors have focused their attention on position estimation accuracy, which wastes the potential of ML techniques to improve other performance parameters (e.g., response time, computational complexity, robustness, scalability or energy efficiency). This study shows that there is a strong trend towards the application of supervised learning. Consequently, there are many potential research opportunities in the use of other learning types, such as unsupervised and reinforcement learning, to improve the performance of pedestrian localization systems.

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Section: Machine Learning In Pedestrian Dead Reckoningmentioning

confidence: 99%

A Survey of Machine Learning in Pedestrian Localization Systems: Applications, Open Issues and Challenges

et al. 2021

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“…Quan et al proposed a convolutional neural network (CNN)-based carrier-phase multipath detection method and decreased the multipath measurement weight to improve static and kinematic positioning accuracy [37]. Luo et al proposed a CNNlong short-term memory (LSTM)-based motion pattern recognition method that was used to recognize the pedestrian motion patterns of smartphones, optimize the mathematical model, and improve positioning robustness and accuracy [38]. Brossard et al proposed a CNN-based noise estimation inertial measurement unit model that adaptively estimated the measurement noise of the inertial measurement unit and optimized the stochastic model [39].…”

Section: Introductionmentioning

confidence: 99%

ConvGRU-MHM: a CNN GRU-enhanced MHM for mitigating GNSS multipath

Tong,

Liu,

Tao

et al. 2024

Meas. Sci. Technol.

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In high-precision global navigation satellite system (GNSS) short-baseline positioning, multipath is the main source of errors. If the station environment is quasi-static, repeat periods of satellites can be utilized to generate time- or space-dependent multipath models to mitigate multipaths. However, two general problems are associated with the multipath models constructed based on satellite mechanics: (1) an accuracy decrease occurs when the above models are applied to multipath mitigation over a long time-span; (2) when constructing the spatial and temporal grids of the satellite-based spatially dependent multipath model, it is challenging to balance computational efficiency and spatial resolution. We propose a convolutional neural network-gated recurrent unit enhanced multipath hemispherical map (ConvGRU-MHM) in the observational domain to address these problems. The proposed method directly mines the deep features of elevation, azimuth angle, and multipath and the mapping relationship between these to establish a real-time prediction model. The predicted multipath is obtained and returned to the observation equation for multipath mitigation when the real-time position of the satellite is placed in the pre-trained model. We compared the multipath mitigation performance of sidereal filtering (SF) and a multipath hemispherical map (MHM) with that of the ConvGRU-MHM to demonstrate the advantages of the proposed method. The experimental results are as follows: (1) in the short time-span (first 20 days), the mean accuracy improvements of the ConvGRU-MHM in the E/N/U direction performed better than those of the SF and MHM; and (2) in the long-term time (after 50 days), the mean accuracy improvements of the ConvGRU-MHM in the E/N/U direction are higher than that of the SF and MHM by 10–20%. As a lightweight model, the ConvGRU-MHM can effectively improve the measurement accuracy of GNSS real-time monitoring in fields, such as deformation monitoring and seismic research.

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“…Edle et al applied BLSTM-RNN to gait detection to improve the stability and recognition ability of the algorithm [ 10 ]. Lin et al [ 11 ] and Luo et al [ 12 ] analyzed the pedestrian directions of forward, backward, left, and right using LSTM and CNN. Wang et al designed a step estimation network based on LSTM, with a stride error rate of

and a walking distance error rate of

[ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Data Fusion of Dual Foot-Mounted INS Based on Human Step Length Model

Chen,

Liu,

Guo

2024

Sensors

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Pedestrian navigation methods based on inertial sensors are commonly used to solve navigation and positioning problems when satellite signals are unavailable. To address the issue of heading angle errors accumulating over time in pedestrian navigation systems that rely solely on the Zero Velocity Update (ZUPT) algorithm, it is feasible to use the pedestrian’s motion constraints to constrain the errors. Firstly, a human step length model is built using human kinematic data collected by the motion capture system. Secondly, we propose the bipedal constraint algorithm based on the established human step length model. Real field experiments demonstrate that, by introducing the bipedal constraint algorithm, the mean biped radial errors of the experiments are reduced by 68.16% and 50.61%, respectively. The experimental results show that the proposed algorithm effectively reduces the radial error of the navigation results and improves the accuracy of the navigation.

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Learning-Based Complex Motion Patterns Recognition for Pedestrian Dead Reckoning

Cited by 12 publications

References 19 publications

A Survey of Machine Learning in Pedestrian Localization Systems: Applications, Open Issues and Challenges

A Survey of Machine Learning in Pedestrian Localization Systems: Applications, Open Issues and Challenges

ConvGRU-MHM: a CNN GRU-enhanced MHM for mitigating GNSS multipath

Data Fusion of Dual Foot-Mounted INS Based on Human Step Length Model

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