scholarly journals Grid-Based Bayesian Filtering Methods for Pedestrian Dead Reckoning Indoor Positioning Using Smartphones

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5343
Author(s):  
Miroslav Opiela ◽  
František Galčík
Keyword(s):  
Dead Reckoning ◽  
Step Length ◽  
Indoor Positioning ◽  
Shopping Mall ◽  
Bayesian Filtering ◽  
Current Position ◽  
Positioning Systems ◽  
Time Calculation ◽  
Pedestrian Dead Reckoning ◽  
Grid Based

Indoor positioning systems for smartphones are often based on Pedestrian Dead Reckoning, which computes the current position from the previously estimated location. Noisy sensor measurements, inaccurate step length estimations, faulty direction detections, and a demand on the real-time calculation introduce the error which is suppressed using a map model and a Bayesian filtering. The main focus of this paper is on grid-based implementations of Bayes filters as an alternative to commonly used Kalman and particle filters. Our previous work regarding grid-based filters is elaborated and enriched with convolution mask calculations. More advanced implementations, the centroid grid filter, and the advanced point-mass filter are introduced. These implementations are analyzed and compared using different configurations on the same raw sensor recordings. The evaluation is performed on three sets of experiments: a custom simple path in faculty building in Slovakia, and on datasets from IPIN competitions from a shopping mall in France, 2018 and a research institute in Italy, 2019. Evaluation results suggests that proposed methods are qualified alternatives to the particle filter. Advantages, drawbacks and proper configurations of these filters are discussed in this paper.

scholarly journals Smartphone-Based Pedestrian Dead Reckoning for 3D Indoor Positioning

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8180
Author(s):  
Jijun Geng ◽  
Linyuan Xia ◽  
Jingchao Xia ◽  
Qianxia Li ◽  
Hongyu Zhu ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Indoor Localization ◽  
Dead Reckoning ◽  
Step Length ◽  
Indoor Positioning ◽  
Actual Measurement ◽  
Location Service ◽  
Cubature Kalman Filter ◽  
Robust Adaptive ◽  
Pedestrian Dead Reckoning

Indoor localization based on pedestrian dead reckoning (PDR) is drawing more and more attention of researchers in location-based services (LBS). The demand for indoor localization has grown rapidly using a smartphone. This paper proposes a 3D indoor positioning method based on the micro-electro-mechanical systems (MEMS) sensors of the smartphone. A quaternion-based robust adaptive cubature Kalman filter (RACKF) algorithm is proposed to estimate the heading of pedestrians based on magnetic, angular rate, and gravity (MARG) sensors. Then, the pedestrian behavior patterns are distinguished by detecting the changes of pitch angle, total accelerometer and barometer values of the smartphone in the duration of effective step frequency. According to the geometric information of the building stairs, the step length of pedestrians and the height difference of each step can be obtained when pedestrians go up and downstairs. Combined with the differential barometric altimetry method, the optimal height can be computed by the robust adaptive Kalman filter (RAKF) algorithm. Moreover, the heading and step length of each step are optimized by the Kalman filter to reduce positioning error. In addition, based on the indoor map vector information, this paper proposes a heading calculation strategy of the 16-wind rose map to improve the pedestrian positioning accuracy and reduce the accumulation error. Pedestrian plane coordinates can be solved based on the Pedestrian Dead-Reckoning (PDR). Finally, combining pedestrian plane coordinates and height, the three-dimensional positioning coordinates of indoor pedestrians are obtained. The proposed algorithm is verified by actual measurement examples. The experimental verification was carried out in a multi-story indoor environment. The results show that the Root Mean Squared Error (RMSE) of location errors is 1.04–1.65 m by using the proposed algorithm for three participants. Furthermore, the RMSE of height estimation errors is 0.17–0.27 m for three participants, which meets the demand of personal intelligent user terminal for location service. Moreover, the height parameter enables users to perceive the floor information.

scholarly journals Maximum convergence algorithm for WiFi based indoor positioning system

2021 ◽  
Vol 11 (5) ◽  
pp. 4027
Author(s):  
Vinh Truong-Quang ◽  
Thong Ho-Sy
Keyword(s):  
Inertial Sensors ◽  
Dead Reckoning ◽  
Access Point ◽  
Indoor Positioning ◽  
Training Data ◽  
Computation Complexity ◽  
Positioning Systems ◽  
Nearest Neighbours ◽  
Indoor Positioning System ◽  
Pedestrian Dead Reckoning

WiFi-based indoor positioning is widely exploited thanks to the existing WiFi infrastructure in buildings and built-in sensors in smartphones. The techniques for indoor positioning require the high-density training data to archive high accuracy with high computation complexity. In this paper, the approach for indoor positioning systems which is called the maximum convergence algorithm is proposed to find the accurate location by the strongest receiver signal in the small cluster and K nearest neighbours (KNN) of other clusters. Also, the K-mean clustering is deployed for each access point to reduce the computation complexity of the offline databases. Moreover, the pedestrian dead reckoning (PDR) method and Kalman filter with the information from the received signal strength (RSS) and inertial sensors are applied to the WiFi fingerprinting to increase the efficiency of the mobile object's position. The different experiments are performed to compare the proposed algorithm with the others using KNN and PDR. The recommended framework demonstrates significant proceed based on the results. The average precision of this system can be lower than 1.02 meters when testing in the laboratory environment with an area of 7x7 m using three access points.

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

Sensors ◽  
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.

scholarly journals Real-Time Indoor Positioning Approach Using iBeacons and Smartphone Sensors

2020 ◽  
Vol 10 (6) ◽  
pp. 2003 ◽  
Author(s):  
Liu Liu ◽  
Bofeng Li ◽  
Ling Yang ◽  
Tianxia Liu
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Dead Reckoning ◽  
Indoor Positioning ◽  
Reasonable Accuracy ◽  
Positioning Accuracy ◽  
Localization Accuracy ◽  
Positioning Systems ◽  
Smartphone Sensors ◽  
Pedestrian Dead Reckoning

For localization in daily life, low-cost indoor positioning systems should provide real-time locations with a reasonable accuracy. Considering the flexibility of deployment and low price of iBeacon technique, we develop a real-time fusion workflow to improve localization accuracy of smartphone. First, we propose an iBeacon-based method by integrating a trilateration algorithm with a specific fingerprinting method to resist RSS fluctuations, and obtain accurate locations as the baseline result. Second, as turns are pivotal for positioning, we segment pedestrian trajectories according to turns. Then, we apply a Kalman filter (KF) to heading measurements in each segment, which improves the locations derived by pedestrian dead reckoning (PDR). Finally, we devise another KF to fuse the iBeacon-based approach with the PDR to overcome orientation noises. We implemented this fusion workflow in an Android smartphone and conducted real-time experiments in a building floor. Two different routes with sharp turns were selected. The positioning accuracy of the iBeacon-based method is RMSE 2.75 m. When the smartphone is held steadily, the fusion positioning tests result in RMSE of 2.39 and 2.22 m for the two routes. In addition, the other tests with orientation noises can still result in RMSE of 3.48 and 3.66 m. These results demonstrate our fusion workflow can improve the accuracy of iBeacon positioning and alleviate the influence of PDR drifting.

scholarly journals A Loose-Coupled Fusion of Inertial and UWB Assisted by a Decision-Making Algorithm for Localization of Emergency Responders

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1463 ◽  
Author(s):  
André G. Ferreira ◽  
Duarte Fernandes ◽  
André P. Catarino ◽  
Ana M. Rocha ◽  
João L. Monteiro
Keyword(s):  
Decision Making ◽  
Indoor Localization ◽  
Dead Reckoning ◽  
Time Of Arrival ◽  
Robust Solution ◽  
Localization Accuracy ◽  
Positioning Systems ◽  
Emergency Responders ◽  
Pedestrian Dead Reckoning ◽  
Non Line Of Sight

Combining different technologies is gaining significant popularity among researchers and industry for the development of indoor positioning systems (IPSs). These hybrid IPSs emerge as a robust solution for indoor localization as the drawbacks of each technology can be mitigated or even eliminated by using complementary technologies. However, fusing position estimates from different technologies is still very challenging and, therefore, a hot research topic. In this work, we pose fusing the ultrawideband (UWB) position estimates with the estimates provided by a pedestrian dead reckoning (PDR) by using a Kalman filter. To improve the IPS accuracy, a decision-making algorithm was developed that aims to assess the usability of UWB measurements based on the identification of non-line-of-sight (NLOS) conditions. Three different data fusion algorithms are tested, based on three different time-of-arrival positioning algorithms, and experimental results show a localization accuracy of below 1.5 m for a 99th percentile.

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