A UWB/Improved PDR Integration Algorithm Applied to Dynamic Indoor Positioning for Pedestrians

Chen, Pengzhan; Chen, Xiaoyue

doi:10.3390/s17092065

Cited by 64 publications

(46 citation statements)

References 20 publications

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“…However, in the indoor environment, because the GNSS signal is blocked by humans, buildings and many other factors, it becomes so weak that the positioning accuracy will be greatly reduced. In order to solve indoor positioning problems, many kinds of method have been studied, such as Wi-Fi positioning [3,4], Bluetooth positioning [5,6], ultra-wideband (UWB) [7,8], radio frequency identification (RFID) [9], infrared [10], ZigBee [11], etc. Every positioning method is capable of providing an indoor location service for people, especially the UWB technology which can reach sub-meter accuracy in the indoor open areas [8].…”

Section: Introductionmentioning

confidence: 99%

Indoor Positioning Integrating PDR/Geomagnetic Positioning Based on the Genetic-Particle Filter

Sun

Wang

et al. 2020

Applied Sciences

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This paper proposes a fusion indoor positioning method that integrates the pedestrian dead-reckoning (PDR) and geomagnetic positioning by using the genetic-particle filter (GPF) algorithm. In the PDR module, the Mahony complementary filter (MCF) algorithm is adopted to estimate the heading angles. To improve geomagnetic positioning accuracy and geomagnetic fingerprint specificity, the geomagnetic multi-features positioning algorithm is devised and five geomagnetic features are extracted as the single-point fingerprint by transforming the magnetic field data into the geographic coordinate system (GCS). Then, an optimization mechanism is designed by using gene mutation and the method of reconstructing a particle set to ameliorate the particle degradation problem in the GPF algorithm, which is used for fusion positioning. Several experiments are conducted to evaluate the performance of the proposed methods. The experiment results show that the average positioning error of the proposed method is 1.72 m and the root mean square error (RMSE) is 1.89 m. The positioning precision and stability are improved compared with the PDR method, geomagnetic positioning, and the fusion-positioning method based on the classic particle filter (PF).

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Section: Introductionmentioning

confidence: 99%

Indoor Positioning Integrating PDR/Geomagnetic Positioning Based on the Genetic-Particle Filter

Sun

Wang

et al. 2020

Applied Sciences

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“…Nowadays, indoor localization has become a common issue for various location-based services and applications. A number of technologies have been proposed for indoor localization, which are based on different principles, such as Wi-Fi [1], geomagnetic [2], ultra wide band (UWB) [3], ultrasound [4] and so on. Among these localization technologies, ultrasound and UWB can be used to estimate the distance between the source and terminals, which can provide accurate localization results.…”

Section: Introductionmentioning

confidence: 99%

An Accurate Visual-Inertial Integrated Geo-Tagging Method for Crowdsourcing-Based Indoor Localization

Liu

Zhang

et al. 2019

Remote Sensing

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One of the unavoidable bottlenecks in the public application of passive signal (e.g., received signal strength, magnetic) fingerprinting-based indoor localization technologies is the extensive human effort that is required to construct and update database for indoor positioning. In this paper, we propose an accurate visual-inertial integrated geo-tagging method that can be used to collect fingerprints and construct the radio map by exploiting the crowdsourced trajectory of smartphone users. By integrating multisource information from the smartphone sensors (e.g., camera, accelerometer, and gyroscope), this system can accurately reconstruct the geometry of trajectories. An algorithm is proposed to estimate the spatial location of trajectories in the reference coordinate system and construct the radio map and geo-tagged image database for indoor positioning. With the help of several initial reference points, this algorithm can be implemented in an unknown indoor environment without any prior knowledge of the floorplan or the initial location of crowdsourced trajectories. The experimental results show that the average calibration error of the fingerprints is 0.67 m. A weighted k-nearest neighbor method (without any optimization) and the image matching method are used to evaluate the performance of constructed multisource database. The average localization error of received signal strength (RSS) based indoor positioning and image based positioning are 3.2 m and 1.2 m, respectively, showing that the quality of the constructed indoor radio map is at the same level as those that were constructed by site surveying. Compared with the traditional site survey based positioning cost, this system can greatly reduce the human labor cost, with the least external information.

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“…Outdoors, the global navigation satellite system (GNSS) provides accurate and reliable locations in open-sky environments, but it faces the challenges of signal blockage and invisibility in an indoor environment. With the development of indoor positioning technology, various positioning methods, based on WiFi [3], Bluetooth [4], ultra-wide band (UWB) [5], and inertial sensors [6], have been applied for personal navigation, emergency rescue, and tracking [7]. With the popularity of smartphones and the development of microelectromechanical system (MEMS), indoor positioning technology based on smartphones has found wide application in pedestrian navigation [8].…”

Section: Introductionmentioning

confidence: 99%

A Novel Method of Adaptive Kalman Filter for Heading Estimation Based on an Autoregressive Model

2019

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With the popularity of smartphones and the development of microelectromechanical system (MEMS), the pedestrian dead reckoning (PDR) algorithm based on the built-in sensors of a smartphone has attracted much research. Heading estimation is the key to obtaining reliable position information. Hence, an adaptive Kalman filter (AKF) based on an autoregressive model (AR) is proposed to improve the accuracy of heading for pedestrian dead reckoning in a complex indoor environment. Our approach uses an autoregressive model to build a Kalman filter (KF), and the heading is calculated by the gyroscope, obtained by the magnetometer, and stored by previous estimates, then are fused to determine the measurement heading. An AKF based on the innovation sequence is used to adaptively adjust the state variance matrix to enhance the accuracy of the heading estimation. In order to suppress the drift of the gyroscope, the heading calculated by the AKF is used to correct the heading calculated by the outputs of the gyroscope if a quasi-static magnetic field is detected. Data were collected using two smartphones. These experiments showed that the average error of two-dimensional (2D) position estimation obtained by the proposed algorithm is reduced by 40.00% and 66.39%, and the root mean square (RMS) is improved by 43.87% and 66.79%, respectively, for these two smartphones.

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A UWB/Improved PDR Integration Algorithm Applied to Dynamic Indoor Positioning for Pedestrians

Cited by 64 publications

References 20 publications

Indoor Positioning Integrating PDR/Geomagnetic Positioning Based on the Genetic-Particle Filter

Indoor Positioning Integrating PDR/Geomagnetic Positioning Based on the Genetic-Particle Filter

An Accurate Visual-Inertial Integrated Geo-Tagging Method for Crowdsourcing-Based Indoor Localization

A Novel Method of Adaptive Kalman Filter for Heading Estimation Based on an Autoregressive Model

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