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

In recent years, using smartphones for indoor positioning has become increasingly popular with consumers. This paper presents an integrated localization technique for inertial and magnetic field sensors to challenge indoor positioning without Wi-Fi signals. For dead-reckoning (DR), attitude angle estimation, step length calculation, and step counting estimation are introduced. Dynamic time warping (DTW) usually calculates the distance between the measured magnetic field and magnetic fingerprint in the database. For DR/Magnetic matching (MM), we creatively propose 3-dimensional dynamic time warping (3DDTW) to calculate the distance. Unlike traditional DTW, 3DDTW extends the original one-dimensional signal to a two-dimensional signal. Finally, the weighted least squares further improves indoor positioning accuracy. In the three different experimental scenarios—teaching building, study room, office building—DR/MM hybrid positioning accuracy is about 3.34 m.

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Combining a Modified Particle Filter Method and Indoor Magnetic Fingerprint Map to Assist Pedestrian Dead Reckoning for Indoor Positioning and Navigation

Sensors ◽

10.3390/s20010185 ◽

2019 ◽

Vol 20 (1) ◽

pp. 185

Author(s):

Fang-Shii Ning ◽

Yu-Chun Chen

Keyword(s):

Magnetic Field ◽

Particle Filter ◽

Dead Reckoning ◽

Step Length ◽

Indoor Positioning ◽

Global Navigation Satellite Systems ◽

Filter Method ◽

Indoor Environments ◽

Positioning Accuracy ◽

Pedestrian Dead Reckoning

Although advancement has been observed in global navigation satellite systems and these systems are widely used, they cannot provide effective navigation and positioning services in covered areas and areas that lack strong signals, such as indoor environments. Therefore, in recent years, indoor positioning technology has become the focus of research and development. The magnetic field of the Earth is quite stable in an open environment. Due to differences in building and internal structures, this type of three-dimensional vector magnetic field is widely available indoors for indoor positioning. A smartphone magnetometer was used in this study to collect magnetic field data for constructing indoor magnetic field maps. Moreover, an acceleration sensor and a gyroscope were used to identify the position of a mobile phone and detect the number of steps travelled by users with the phone. This study designed a procedure for measuring the step length of users. All obtained information was input into a pedestrian dead reckoning (PDR) algorithm for calculating the position of the device. The indoor positioning accuracy of the PDR algorithm was optimised using magnetic gradients of magnetic field maps with a modified particle filter algorithm. Experimental results reveal that the indoor positioning accuracy was between 0.6 and 0.8 m for a testing area that was 85 m long and 33 m wide. This study effectively improved the indoor positioning accuracy and efficiency by using the particle filter method in combination with the PDR algorithm with the magnetic fingerprint map.

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An Efficient Position Estimation of Indoor Positioning System Based on Dynamic Time Warping

International Journal of Advanced Trends in Computer Science and Engineering ◽

10.30534/ijatcse/2020/0491.22020 ◽

2020 ◽

Vol 9 (1.2) ◽

pp. 17-20

Author(s):

Rhowel M. Dellosa

Keyword(s):

Dynamic Time Warping ◽

Indoor Positioning ◽

Position Estimation ◽

Positioning System ◽

Time Warping ◽

Dynamic Time ◽

Indoor Positioning System

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Magnetic Field as a Characterization of Wide and Narrow Spaces in a Real Challenging Scenario Using Dynamic Time Warping

2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN) ◽

10.1109/ipin.2018.8533741 ◽

2018 ◽

Author(s):

Antoni Perez-Navarro ◽

Raul Montoliu ◽

Joaquin Torres-Sospedra ◽

Jordi Conesa

Keyword(s):

Magnetic Field ◽

Dynamic Time Warping ◽

Time Warping ◽

Dynamic Time

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CC-DTW: An Accurate Indoor Fingerprinting Localization Using Calibrated Channel State Information and Modified Dynamic Time Warping

Sensors ◽

10.3390/s19091984 ◽

2019 ◽

Vol 19 (9) ◽

pp. 1984 ◽

Cited By ~ 3

Author(s):

Zhongliang Deng ◽

Xiao Fu ◽

Qianqian Cheng ◽

Lingjie Shi ◽

Wen Liu

Keyword(s):

Channel State Information ◽

Dynamic Time Warping ◽

Local Area Network ◽

Location Based Services ◽

Area Network ◽

Time Warping ◽

Channel State ◽

Positioning Accuracy ◽

State Information ◽

Dynamic Time

Indoor wireless local area network (WLAN) based positioning technologies have boomed recently because of the huge demands of indoor location-based services (ILBS) and the wide deployment of commercial Wi-Fi devices. Channel state information (CSI) extracted from Wi-Fi signals could be calibrated and utilized as a fine-grained positioning feature for indoor fingerprinting localization. One of the main factors that would restrict the positioning accuracy of fingerprinting systems is the spatial resolution of fingerprints (SRF). This paper mainly focuses on the improvement of SRF for indoor CSI-based positioning and a calibrated CSI feature (CCF) with high SRF is established based on the preprocess of both measured amplitude and phase. In addition, a similarity calculation metric for the proposed CCF is designed based on modified dynamic time warping (MDTW). An indoor fingerprinting method based on CCF and MDTW, named CC-DTW, is then proposed to improve the positioning accuracy in indoors. Experiments are conducted in two indoor office testbeds, and the performances of the proposed CC-DTW, one time-reversal (TR) based approach and one Euclidean distance (ED) based approach are evaluated and discussed. The results show that the SRF of CC-DTW outperforms the TR-based one and the ED-based one in both two testbeds in terms of the receiver operating characteristic (ROC) curve metric, and the area under curve (AUC) metric.

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Indoor Trajectory Reconstruction of Walking, Jogging, and Running Activities Based on a Foot-Mounted Inertial Pedestrian Dead-Reckoning System

Sensors ◽

10.3390/s20030651 ◽

2020 ◽

Vol 20 (3) ◽

pp. 651 ◽

Cited By ~ 1

Author(s):

Jesus D. Ceron ◽

Christine F. Martindale ◽

Diego M. López ◽

Felix Kluge ◽

Bjoern M. Eskofier

Keyword(s):

Dynamic Time Warping ◽

Dead Reckoning ◽

Detection Algorithm ◽

Time Warping ◽

Trajectory Reconstruction ◽

Position Errors ◽

Controlled Environments ◽

Dynamic Time ◽

Pedestrian Dead Reckoning ◽

New Algorithms

The evaluation of trajectory reconstruction of the human body obtained by foot-mounted Inertial Pedestrian Dead-Reckoning (IPDR) methods has usually been carried out in controlled environments, with very few participants and limited to walking. In this study, a pipeline for trajectory reconstruction using a foot-mounted IPDR system is proposed and evaluated in two large datasets containing activities that involve walking, jogging, and running, as well as movements such as side and backward strides, sitting, and standing. First, stride segmentation is addressed using a multi-subsequence Dynamic Time Warping method. Then, detection of Toe-Off and Mid-Stance is performed by using two new algorithms. Finally, stride length and orientation estimation are performed using a Zero Velocity Update algorithm empowered by a complementary Kalman filter. As a result, the Toe-Off detection algorithm reached an F-score between 90% and 100% for activities that do not involve stopping, and between 71% and 78% otherwise. Resulting return position errors were in the range of 0.5% to 8.8% for non-stopping activities and 8.8% to 27.4% otherwise. The proposed pipeline is able to reconstruct indoor trajectories of people performing activities that involve walking, jogging, running, side and backward walking, sitting, and standing.

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