IMPROVED INDOOR POSITIONING USING BLE DIFFERENTIAL DISTANCE CORRECTION AND PEDESTRIAN DEAD RECKONING

Abstract. Recently, indoor positioning becomes a popular issue because of its corresponding location-aware applications. Owing to the limits of the sheltered signal of satellites in indoor environments, one of the alternative scheme is Bluetooth Low Energy (BLE) technology. BLE device broadcasts Received Signal Strength Indicator (RSSI) for distance estimation and further positioning. However, in the complex indoor environment, the reflection, fading, and multipath effect of BLE make the variable RSSI and may lead to poor quality of RSSI. In this study, the concept called Differential Distance Correction (DDC) is similar to the Differential Global Navigation Satellite System (DGNSS). This method can eliminate some residuals and further improve the results with the corrected distance. On the other hand, Pedestrian Dead Reckoning (PDR) is another common indoor positioning method. PDR can propagate the next position from the current position by the implemented of inertial sensors. Despite that, the error of inertial sensors would accumulate with time and walking distance, which position update is required for restraining the drift. Accordingly, the two indoor positioning methods have their strong and weak point. BLE-based positioning is absolute positioning, while PDR is relative positioning. This study proposes a concept that combines the two methods. The pedestrian receives the RSSI and records the information from inertial sensors simultaneously. Through the complementary of two methods, the positioning results would be improved from 29% to 66% according to different travelled distance.

Download Full-text

Multimode Pedestrian Dead Reckoning Gait Detection Algorithm Based on Identification of Pedestrian Phone Carrying Position

Mobile Information Systems ◽

10.1155/2019/4709501 ◽

2019 ◽

Vol 2019 ◽

pp. 1-14 ◽

Cited By ~ 1

Author(s):

Ying Guo ◽

Qinghua Liu ◽

Xianlei Ji ◽

Shengli Wang ◽

Mingyang Feng ◽

...

Keyword(s):

Inertial Sensors ◽

Distance Estimation ◽

Dead Reckoning ◽

Principal Component ◽

Detection Algorithm ◽

Estimation Accuracy ◽

Indoor Environments ◽

Using Data ◽

The Time Domain ◽

Pedestrian Dead Reckoning

Pedestrian dead reckoning (PDR) is an essential technology for positioning and navigation in complex indoor environments. In the process of PDR positioning and navigation using mobile phones, gait information acquired by inertial sensors under various carrying positions differs from noise contained in the heading information, resulting in excessive gait detection deviation and greatly reducing the positioning accuracy of PDR. Using data from mobile phone accelerometer and gyroscope signals, this paper examined various phone carrying positions and switching positions as the research objective and analysed the time domain characteristics of the three-axis accelerometer and gyroscope signals. A principal component analysis algorithm was used to reduce the dimension of the extracted multidimensional gait feature, and the extracted features were random forest modelled to distinguish the phone carrying positions. The results show that the step detection and distance estimation accuracy in the gait detection process greatly improved after recognition of the phone carrying position, which enhanced the robustness of the PDR algorithm.

Download Full-text

Maximum convergence algorithm for WiFi based indoor positioning system

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v11i5.pp4027-4036 ◽

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.

Download Full-text

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.

Download Full-text

An Experimental Study of an Improved BLE Indoor Positioning Scheme Based on Differential Distance Correction

Proceedings of the 31st International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2018) ◽

10.33012/2018.15905 ◽

2018 ◽

Author(s):

Yun-Tzu Kuo

Keyword(s):

Experimental Study ◽

Indoor Positioning ◽

Differential Distance ◽

Distance Correction

Download Full-text

Magnetometer bias insensitive magnetic field matching based on pedestrian dead reckoning for smartphone indoor positioning

IEEE Sensors Journal ◽

10.1109/jsen.2021.3073397 ◽

2021 ◽

pp. 1-1

Author(s):

Jian Kuang ◽

Taiyu Li ◽

Xiaoji Niu

Keyword(s):

Magnetic Field ◽

Dead Reckoning ◽

Indoor Positioning ◽

Field Matching ◽

Pedestrian Dead Reckoning

Download Full-text

Leveraging Visual Place Recognition to Improve Indoor Positioning with Limited Availability of WiFi Scans

Sensors ◽

10.3390/s19173657 ◽

2019 ◽

Vol 19 (17) ◽

pp. 3657 ◽

Cited By ~ 1

Author(s):

Michał R. Nowicki ◽

Piotr Skrzypczyński

Keyword(s):

Indoor Localization ◽

Dead Reckoning ◽

Indoor Positioning ◽

Positioning System ◽

Indoor Environments ◽

University Campus ◽

Sources Of Information ◽

Visual Place Recognition ◽

Indoor Positioning System

WiFi-based fingerprinting is promising for practical indoor localization with smartphones because this technique provides absolute estimates of the current position, while the WiFi infrastructure is ubiquitous in the majority of indoor environments. However, the application of WiFi fingerprinting for positioning requires pre-surveyed signal maps and is getting more restricted in the recent generation of smartphones due to changes in security policies. Therefore, we sought new sources of information that can be fused into the existing indoor positioning framework, helping users to pinpoint their position, even with a relatively low-quality, sparse WiFi signal map. In this paper, we demonstrate that such information can be derived from the recognition of camera images. We present a way of transforming qualitative information of image similarity into quantitative constraints that are then fused into the graph-based optimization framework for positioning together with typical pedestrian dead reckoning (PDR) and WiFi fingerprinting constraints. Performance of the improved indoor positioning system is evaluated on different user trajectories logged inside an office building at our University campus. The results demonstrate that introducing additional sensing modality into the positioning system makes it possible to increase accuracy and simultaneously reduce the dependence on the quality of the pre-surveyed WiFi map and the WiFi measurements at run-time.

Download Full-text

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

Sensors ◽

10.3390/s20185343 ◽

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.

Download Full-text