Implementation and performance analysis of the PDR/GNSS integration on a smartphone

Jiang, Changhui; Chen, Yuwei; Chen, Chen; Jia, Jianxin; Sun, Hai‐Wei; Wang, Tinghuai; Hyyppä, Juha

doi:10.1007/s10291-022-01260-0

Cited by 27 publications

(11 citation statements)

References 33 publications

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“…Step length is usually estimated through processing the three-axis accelerometer measurements. The step length estimation model is expressed as follows [23,24]:…”

Section: Pdr Mechanismmentioning

confidence: 99%

A Probabilistic Method-Based Smartphone GNSS Fault Detection and Exclusion System Utilizing PDR Step Length

Jiang,

Chen,

Liu

et al. 2023

Remote Sensing

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A smartphone equipped with a Global Navigation Satellite System (GNSS) module can generate positional information for location-based services. However, GNSS signals are susceptible to fragility, multipath (MP), and Non-Line-Of-Sight (NLOS) interference, which can lead to a degradation in the accuracy of GNSS positioning on smartphones. Due to limitations in the smartphone’s antenna, GNSS signal strength is typically lower. Moreover, in urban areas, where smartphones rely on GNSS, MP and NLOS signals are the primary factors impeding accurate positioning. In this paper, with the goal of enhancing both the accuracy and robustness of smartphone GNSS positioning, we propose two methods. Firstly, an optimized particle filter method employing a Krill Herd Algorithm (KHA) is suggested for the integration of GNSS and Pedestrian Dead Reckoning (PDR). Secondly, a probabilistic approach is presented to identify faulty GNSS measurements using step distance information obtained from the PDR. Experimental tests were conducted using smartphones to evaluate the performance of the proposed method. The results demonstrate that both the KHA and fault detection methods effectively enhance the performance of integrated PDR and GNSS.

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“…Step length is usually estimated through processing the three-axis accelerometer measurements. The step length estimation model is expressed as follows [23,24]:…”

Section: Pdr Mechanismmentioning

confidence: 99%

A Probabilistic Method-Based Smartphone GNSS Fault Detection and Exclusion System Utilizing PDR Step Length

Jiang,

Chen,

Liu

et al. 2023

Remote Sensing

Self Cite

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“…However, the problem of computational complexity has not been solved. Jiang Changhui and others used graphic optimisation method to replace Kalman filter for vector tracking, and realised PDR/GNSS of smart phones through Kalman filter and factor graph optimisation (FGO), but it was still limited by the shortcomings of GNSS and could not be applied in underground mines [20,21]. The particle filter uses limited particles to draw the distribution curve to deal with a non-linear non-Gaussian environment, but it is often very difficult to draw the distribution curve in a mine environment.…”

Section: Related Workmentioning

confidence: 99%

Height information aided 3D real‐time large‐scale underground user positioning

Tang

Zhang

et al. 2022

IET Radar Sonar & Navi

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Due to the cost of inertial navigation and visual navigation equipment and lake of satellite navigation signals, they cannot be used in large-scale underground mining environment. To solve this problem, this study proposes a large-scale underground 3D real-time positioning method with seam height assistance. This method uses the ultra wide band positioning base station as the core and is combined with seam height information to build a factor graph confidence transfer model to realise 3D positioning. The simulation results show that the proposed real-time method is superior to the existing algorithms in positioning accuracy and can meet the needs of large-scale underground users.

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“…Similarly, Sun et al [ 20 ] proposed a motion-model-assisted fusion algorithm based on GNSS/MEMS that detected gross errors through a constant yaw rate and velocity model and the chi-square test. Moreover, Jiang et al [ 21 ] realized the GNSS/PDR fusion positioning based on Kalman filter and graph optimization, finding that graph optimization can significantly improve positioning accuracy. The above studies have shown that the use of fusion positioning in complex urban environments is very helpful in improving the stability and reliability of positioning results.…”

Section: Introductionmentioning

confidence: 99%

An Effective GNSS/PDR Fusion Positioning Algorithm on Smartphones for Challenging Scenarios

Zhang,

Yu,

et al. 2024

Sensors

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The location-based smartphone service brings new development opportunities for seamless indoor/outdoor positioning. However, in complex scenarios such as cities, tunnels, overpasses, forests, etc., using only GNSS on smartphones cannot provide stable and reliable positioning results. Usually, additional sensors are needed to assist GNSS. This paper investigates the GNSS positioning algorithm assisted by pedestrian dead reckoning (PDR) in complex scenarios. First, we introduce a step detection algorithm based on the peak–valley of acceleration modulus, and the Weinberg model and the Mahony algorithm in PDR are used to estimate step length and heading. On this basis, we evaluated the performance of GNSS/PDR fusion positioning in an open scenario, a semiopen scenario, and a blocked scenario, respectively. Finally, we develop a GNSS/PDR real-time positioning software, called China University of Mining and Technology-POSitioning (CUMT-POS) version 1.0, on the Android 10 platform. By comparing GNSS solutions, PDR solutions, GNSS/PDR solutions, and real-time kinematic (RTK) solutions, we verify the potential auxiliary ability of PDR for GNSS positioning in complex environments, proving that multisource sensor fusion positioning significantly improves reliability and stability. Our research can help the realization of urban informatization and smart cities.

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Implementation and performance analysis of the PDR/GNSS integration on a smartphone

Cited by 27 publications

References 33 publications

A Probabilistic Method-Based Smartphone GNSS Fault Detection and Exclusion System Utilizing PDR Step Length

A Probabilistic Method-Based Smartphone GNSS Fault Detection and Exclusion System Utilizing PDR Step Length

Height information aided 3D real‐time large‐scale underground user positioning

An Effective GNSS/PDR Fusion Positioning Algorithm on Smartphones for Challenging Scenarios

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