scholarly journals Hybrid Onboard Smartphone Sensors Measurements to Improve Heading Estimation for Indoors Positioning Solutions

2019 ◽  
Vol 4 (2) ◽  
pp. 50-60 ◽  
Author(s):  
Haval Darwesh Abdalkarim ◽  
Halgurd Sarhang Maghdid
Keyword(s):  
Angular Displacement ◽  
Dead Reckoning ◽  
Indoor Positioning ◽  
Indoor Navigation ◽  
Positioning Accuracy ◽  
Position Information ◽  
Precise Position ◽  
Current Location ◽  
Smartphone Sensors ◽  
Heading Estimation

In the last decade, there is a significant progression and huge demand in using technology; specifically, those technologies are embedded in smartphones (SP). Examples of these technologies are embedding various sensors for multi-purposes. Positioning sensors (Accelerometer, Gyroscope, and Magnetometer) are one of the significant technologies. Besides this, indoor positioning services on smartphones are the main advantage of these sensors. There are many indoor positioning applications, for instance; billing, shopping, security and safety, indoor navigation, entertainment applications, and other point-of-interest (POI) applications. Nevertheless, precise position information through current positioning techniques is the main issue of these applications. The pedestrian dead reckoning (PDR) technique is one of the techniques in which the integration of onboard sensors is used for locating smartphones. Estimated distance, heading, and typical speed can be measured to determine the estimated position of the smartphone via using the PDR technique. The PDR technique offers a low positioning accuracy due to existing accumulated errors of the embedded sensors. To solve this issue, this article proposes a hybrid multi-sensors measurement to reduce the existing sensors drifts and errors and to increase estimated heading accuracy of the smartphone. Further, the sensors’ measurements with the previously estimated position are fused by using KALMAN Filter to determine the current location of the smartphone in each step of walking with better angular displacement accuracy. Proposed algorithm depends on increasing estimated angular displacement of the smartphone using combination of the integrated sensors’ measurements. The achieved positioning accuracy through the proposed approach and based on trial experiments is around 2 meters, which is equivalent to 10% improvement in comparison with state of the art.

Design of Continuous Indoor Navigation System Based on INS and Wifi

2013 ◽  
Vol 303-306 ◽  
pp. 2046-2049 ◽  
Author(s):  
Yi Hu ◽  
Lei Sheng ◽  
Shan Jun Zhang
Keyword(s):  
Navigation System ◽  
Smart Phone ◽  
Dead Reckoning ◽  
Indoor Positioning ◽  
Indoor Navigation ◽  
High Price ◽  
Position Information ◽  
Positioning Method ◽  
Indoor Navigation System ◽  
Navigation Service

The application of navigation, such as guidance of pedestrians, requires a certain accuracy of continuous outdoor and indoor positioning. In outdoor environments GPS system has proved to be effective. However in indoor it is challenging to control the accuracy within 2 to 3 meters. At present several approaches have been developed for indoor positioning, such as RFID. But they are mainly been implemented in professional areas, for general user such as tourists and visual incapable users it is difficult to take advantage of these technologies because of the high price of terminal and the navigation service covered area is extremely limited. In this paper, a new approach of indoor navigation method is proposed to solve the problems of traditional methods. It is based on INS and wifi positioning technology. As hardware, wifi receiver, smart phone built-in accelerometer and digital compass are selected and investigated. User’s indoor position is first estimated by dead reckoning method with INS navigation system and then be recalibrated by wifi position information. Several experiments performed in the test verified the effectiveness of this indoor continuous positioning method described in this paper.

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 Gravity-Based Methods for Heading Computation in Pedestrian Dead Reckoning

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1170 ◽  
Author(s):  
Adi Manos ◽  
Itzik Klein ◽  
Tamir Hazan
Keyword(s):  
Field Experiments ◽  
Dead Reckoning ◽  
Vertical Direction ◽  
Indoor Navigation ◽  
Magnetic Sensors ◽  
Dominant Factor ◽  
Heading Estimation ◽  
Using Data ◽  
Pedestrian Dead Reckoning ◽  
Gravity Direction

One of the common ways for solving indoor navigation is known as Pedestrian Dead Reckoning (PDR), which employs inertial and magnetic sensors typically embedded in a smartphone carried by a user. Estimation of the pedestrian’s heading is a crucial step in PDR algorithms, since it is a dominant factor in the positioning accuracy. In this paper, rather than assuming the device to be fixed in a certain orientation on the pedestrian, we focus on estimating the vertical direction in the sensor frame of an unconstrained smartphone. To that end, we establish a framework for gravity direction estimation and highlight the important role it has for solving the heading in the horizontal plane. Furthermore, we provide detailed derivation of several approaches for calculating the heading angle, based on either the gyroscope or the magnetic sensor, all of which employ the estimated vertical direction. These various methods—both for gravity direction and for heading estimation—are demonstrated, analyzed and compared using data recorded from field experiments with commercial smartphones.

scholarly journals INDOOR POSITIONING USING WLAN FINGERPRINT MATCHING AND PATH ASSESSMENT WITH RETROACTIVE ADJUSTMENT ON MOBILE DEVICES

2018 ◽  
Vol XLII-4 ◽  
pp. 253-260
Author(s):  
E. Gulo ◽  
G. Sohn ◽  
A. Afnan
Keyword(s):  
Mobile Devices ◽  
Inertial Sensor ◽  
Dead Reckoning ◽  
Indoor Positioning ◽  
Sensor Data ◽  
Fingerprint Matching ◽  
Positioning Accuracy ◽  
Viable Approach ◽  
Similar Accuracy ◽  
Pedestrian Dead Reckoning

<p><strong>Abstract.</strong> With the increasing number and usage of mobile devices in people’s daily life, indoor positioning has attracted a lot attention from both academia and industry for the purpose of providing location-aware services. This work proposes an indoor positioning system, primarily based on WLAN fingerprint matching, that includes various minor improvements to improve the positioning accuracy of the algorithm, as well as improve the quality and reduce the collection time of the reference fingerprints. In addition, a novel Path Evaluation and Retroactive Adjustment module is employed; it intends to improve the positioning accuracy of the system in a similar fashion to a Pedestrian Dead Reckoning implemented along with WLAN Fingerprint Matching in a Sensor Fusion system. The benefit of this approach being that it avoids the requirement of inertial sensor data, as well as its intensive computation and power use, while providing a similar accuracy improvement to Pedestrian Dead Reckoning. Our experimental results demonstrate that this may be a viable approach for positioning using mobile devices in an indoor environment.</p>

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

Electronics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 185 ◽  
Author(s):  
Jian Chen ◽  
Gang Ou ◽  
Ao Peng ◽  
Lingxiang Zheng ◽  
Jianghong Shi
Keyword(s):  
Magnetic Field ◽  
Dynamic Time Warping ◽  
Weighted Least Squares ◽  
Dead Reckoning ◽  
Step Length ◽  
Indoor Positioning ◽  
Time Warping ◽  
Positioning Accuracy ◽  
3 Dimensional ◽  
Dynamic Time

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.

scholarly journals An Indoor Robust Localization Algorithm Based on Data Association Technique

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6598
Author(s):  
Long Cheng ◽  
Yong Wang ◽  
Mingkun Xue ◽  
Yangyang Bi
Keyword(s):  
Hypothesis Testing ◽  
Indoor Localization ◽  
Likelihood Estimation ◽  
Indoor Positioning ◽  
Position Estimation ◽  
Line Of Sight ◽  
Localization Algorithm ◽  
Positioning Accuracy ◽  
Position Information ◽  
Position Estimate

As a key technology of the Internet of Things, wireless sensor network (WSN) has been used widely in indoor localization systems. However, when the sensor is transmitting signals, it is affected by the non-line-of-sight (NLOS) transmission, and the accuracy of the positioning result is decreased. Therefore, solving the problem of NLOS positioning has become a major focus for indoor positioning. This paper focuses on solving the problem of NLOS transmission that reduces positioning accuracy in indoor positioning. We divided the anchor nodes into several groups and obtained the position information of the target node for each group through the maximum likelihood estimation (MLE). By identifying the NLOS method, a part of the position estimates polluted by NLOS transmission was discarded. For the position estimates that passed the hypothesis testing, a corresponding poly-probability matrix was established, and the probability of each position estimate from line-of-sight (LOS) and NLOS was calculated. The position of the target was obtained by combining the probability with the position estimate. In addition, we also considered the case where there was no continuous position estimation through hypothesis testing and through the NLOS tracking method to avoid positioning errors. Simulation and experimental results show that the algorithm proposed has higher positioning accuracy and higher robustness than other algorithms.

scholarly journals Near Relation-Based Indoor Positioning Method under Sparse Wi-Fi Fingerprints

2020 ◽  
Vol 9 (12) ◽  
pp. 714
Author(s):  
Yankun Wang ◽  
Renzhong Guo ◽  
Weixi Wang ◽  
Xiaoming Li ◽  
Shengjun Tang ◽  
...  
Keyword(s):  
Interpolation Method ◽  
Indoor Positioning ◽  
Kriging Interpolation ◽  
Fingerprint Matching ◽  
Positioning Accuracy ◽  
Position Information ◽  
Kriging Interpolation Method ◽  
Additional Equipment ◽  
Positioning Method ◽  
Fingerprint Database

Indoor positioning is of great importance in the era of mobile computing. Currently, considerable focus has been on RSS-based locations because they can provide position information without additional equipment. However, this method suffers from two challenges: (1) fingerprint ambiguity and (2) labour-intensive fingerprint collection. To overcome these drawbacks, we provide a near relation-based indoor positioning method under a sparse Wi-Fi fingerprint. To effectively obtain the fingerprint database, certain interpolation methods are used to enrich sparse Wi-Fi fingerprints. A near relation boundary is provided, and Wi-Fi fingerprints are constrained to this region to reduce fingerprint ambiguity, which can also improve the efficiency of fingerprint matching. Extensive experiments show that the kriging interpolation method performs well, and a positioning accuracy of 2.86 m can be achieved with a near relation under a 1 m interpolation density.

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

2019 ◽  
Vol 9 (18) ◽  
pp. 3727
Author(s):  
Chai ◽  
Chen ◽  
Wang
Keyword(s):  
Kalman Filter ◽  
Autoregressive Model ◽  
Dead Reckoning ◽  
Position Estimation ◽  
Average Error ◽  
Adaptive Kalman Filter ◽  
Position Information ◽  
Variance Matrix ◽  
Heading Estimation ◽  
Pedestrian Dead Reckoning

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.

scholarly journals Implementation of Hybrid Indoor Positioning System based on Wi-Fi and PDR in Smartphone

2019 ◽  
Vol 8 (2S6) ◽  
pp. 357-361
Keyword(s):  
Kalman Filter ◽  
Inertial Sensors ◽  
Dead Reckoning ◽  
Indoor Positioning ◽  
Initial Position ◽  
Mobile User ◽  
Sensor Data ◽  
Positioning System ◽  
Fusion Algorithm ◽  
Positioning Accuracy

This paper proposed thehybridindoor positioning system in smartphone for positioning accuracy by fusion of wireless-fidelity (Wi-Fi) signals and inertial sensors from pedestrian dead reckoning (PDR) in smartphone. The proposed system uses Wi-Fi as the source of received signal strength indicator (RSSI) for fingerprint and smartphones sensor data from PDR. RSSI signals are used to determine the initial position and reduce error accumulation of PDR while smartphone sensor data are used to estimate user trajectory. Extended Kalman Filter (EKF) is the fusion algorithm used for its similarity with Kalman Filter (KF) but with advantages of processing non-linear progressions. An estimated 49 steps were detected which is identical to the 50 steps taken in the experiment while showing a trajectory similar to the actual route taken by the mobile user. A benefit of using built-in smartphone sensors is its cost-effectiveness and availability that does not require additional hardware. In addition, a nonlinear EKF is used to enhance the positioning accuracy in the proposed system. Further studies will be made in the potential of indoor positioning algorithm including the effect of noise interference on sensors and RSSI and the accumulated errors resulting from walking

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.

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