A Hybrid Dead Reckon System Based on 3-Dimensional Dynamic Time Warping

Chen, Jian; Ou, Gang; Peng, Ao; Li, Zheng; Shi, Jianghong

doi:10.3390/electronics8020185

Cited by 16 publications

(11 citation statements)

References 33 publications

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“…As we often obtained more than three signals in the experiment, we faced the problem of overdetermination, which can generally be solved using the least-squares method [3,28]. The least-squares (LS) method is a mathematical optimization technique that obtains the best match for the data by minimizing the sum of the squares of the errors.…”

Section: Methodsmentioning

confidence: 99%

Probability-Based Indoor Positioning Algorithm Using iBeacons

Xia

Liu

et al. 2019

Sensors

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High-precision indoor positioning is important for modern society. This paper proposes a way to achieve high positioning accuracy and obtain a trajectory close to the actual path in a common application scenario by smartphone without the use of a complicated algorithm. In the actual positioning process, a stable signal source can reduce the signal interference caused by environments. Bluetooth low energy has its own advantages in indoor positioning because it can be seen as a more stable signal source. In this study, we used smartphones to record the changing Bluetooth signals and used a basic nearest neighbor, weight centroid, and probability-based method, which we called an advanced weighted centroid method, to obtain position coordinates and the motion trajectory during the experiment. We used a weight centroid method based on least squares to solve the overdetermined problem. This can also be used to calculate the initial position of the advanced weight centroid. The advanced weighted centroid method introduced a Gaussian distribution to model the distribution of the signal. Translating a deterministic problem into a fuzzy probability problem aligns more with positioning facts and can achieve better results. Experimental results showed that the root-mean-square error (RMSE) of the dynamic positioning result obtained through the probabilistic method was within 1 m and had a more consistent trajectory. Moreover, the impact of the number of iBeacons on the positioning accuracy has been discussed, and a reference for iBeacon placement has been provided. In addition, an experiment was also conducted on the effect of signal transmission frequency on accuracy.

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Section: Methodsmentioning

confidence: 99%

Probability-Based Indoor Positioning Algorithm Using iBeacons

Xia

Liu

et al. 2019

Sensors

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“…DTW usually calculates the distance between the measured magnetic field and the magnetic fingerprint in the database. Based on the traditional DTW, Chen et al [100] proposed an improved DTW (3DDTW) that extends the one-dimensional input signal into a two-dimensional one. Then, 3DDTW was used to calculate the distance between the measured magnetic field and the magnetic fingerprint, thus reducing the mismatch of the magnetic fingerprint.…”

Section: Dynamic Time Warpingmentioning

confidence: 99%

A Survey of Magnetic-Field-Based Indoor Localization

Ouyang

Abed-Meraim

2022

Electronics

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Magnetic fields have attracted considerable attention in indoor localization due to their ubiquitous and infrastructure-free characteristics. This survey provides a comprehensive review of magnetic-field-based indoor localization methods. We first introduce characteristics of the magnetic field, its advantages, and its challenges. We then describe the magnetometer model and the effect of ferromagnetic interference. We also present coordinate systems commonly used for magnetic field localization and describe their transformation relationships. We then compare the existing publicly available magnetic field benchmark datasets, present magnetometer calibration algorithms, and show how efficiently magnetic field maps can be built. We also summarize state-of-the-art magnetic field localization methods (e.g., magnetic landmarks, dynamic time warping, magnetic fingerprinting, filters, simultaneous localization and mapping, and neural network). The smartphone-based pedestrian dead reckoning approach is also reviewed.

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“…By averaging the direction of travel for all steps between two successive detected turns, the authors reduced fluctuations in the direction of travel caused by user movement, sensor noise, local magnetic anomalies, and other electrical disturbances. In addition, 3DDTW [ 54 ], which extends the one-dimensional input signal into a two-dimensional signal, calculates the distance between the MF measurement and the magnetic fingerprinting, thus reducing the mismatch between the magnetic signature. CSMS [ 55 ] integrates channel state information (CSI) and magnetic field strength (MFS).…”

Section: Related Workmentioning

confidence: 99%

Analysis of Magnetic Field Measurements for Indoor Positioning

Ouyang

Abed-Meraim

2022

Sensors

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Infrastructure-free magnetic fields are ubiquitous and have attracted tremendous interest in magnetic field-based indoor positioning. However, magnetic field-based indoor positioning applications face challenges such as low discernibility, heterogeneous devices, and interference from ferromagnetic materials. This paper first analyzes the statistical characteristics of magnetic field (MF) measurements from heterogeneous smartphones. It demonstrates that, in the absence of disturbances, the MF measurements in indoor environments follow a Gaussian distribution with temporal stability and spatial discernibility. It shows the fluctuations in magnetic field intensity caused by the rotation of a smartphone around the Z-axis. Secondly, it suggests that the RLOWESS method can be used to eliminate magnetic field anomalies, using magnetometer calibration to ensure consistent MF measurements in heterogeneous smartphones. Thirdly, it tests the magnetic field positioning performance of homogeneous and heterogeneous devices using different machine learning methods. Finally, it summarizes the feasibility/limitations of using only MF measurement for indoor positioning.

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A Hybrid Dead Reckon System Based on 3-Dimensional Dynamic Time Warping

Cited by 16 publications

References 33 publications

Probability-Based Indoor Positioning Algorithm Using iBeacons

Probability-Based Indoor Positioning Algorithm Using iBeacons

A Survey of Magnetic-Field-Based Indoor Localization

Analysis of Magnetic Field Measurements for Indoor Positioning

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