scholarly journals Magnetic-Map-Matching-Aided Pedestrian Navigation Using Outlier Mitigation Based on Multiple Sensors and Roughness Weighting

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4782 ◽  
Author(s):  
Yong Hun Kim ◽  
Min Jun Choi ◽  
Eung Ju Kim ◽  
Jin Woo Song
Keyword(s):  
Magnetic Field ◽  
Importance Sampling ◽  
Dead Reckoning ◽  
Position Error ◽  
Magnetic Sensors ◽  
Map Matching ◽  
Multiple Sensors ◽  
Pedestrian Navigation ◽  
Zero Velocity ◽  
Pedestrian Dead Reckoning

This research proposes an algorithm that improves the position accuracy of indoor pedestrian dead reckoning, by compensating the position error with a magnetic field map-matching technique, using multiple magnetic sensors and an outlier mitigation technique based on roughness weighting factors. Since pedestrian dead reckoning using a zero velocity update (ZUPT) does not use position measurements but zero velocity measurements in a stance phase, the position error cannot be compensated, which results in the divergence of the position error. Therefore, more accurate pedestrian dead reckoning is achievable when the position measurements are used for position error compensation. Unfortunately, the position information cannot be easily obtained for indoor navigation, unlike in outdoor navigation cases. In this paper, we propose a method to determine the position based on the magnetic field map matching by using the importance sampling method and multiple magnetic sensors. The proposed method does not simply integrate multiple sensors but uses the normalization and roughness weighting method for outlier mitigation. To implement the indoor pedestrian navigation algorithm more accurately than in existing indoor pedestrian navigation, a 15th-order error model and an importance-sampling extended Kalman filter was utilized to correct the error of the map-matching-aided pedestrian dead reckoning (MAPDR). To verify the performance of the proposed indoor MAPDR algorithm, many experiments were conducted and compared with conventional pedestrian dead reckoning. The experimental results show that the proposed magnetic field MAPDR algorithm provides clear performance improvement in all indoor environments.

Research on WPS/PDR/MM Integrated Algorithm for Pedestrian Navigation and Positioning

2013 ◽  
Vol 437 ◽  
pp. 870-875 ◽  
Author(s):  
Zhong Liang Deng ◽  
Fei Peng Xie ◽  
Yan Pei Yu ◽  
Xiao Hong Zhao ◽  
Zhuang Yuan
Keyword(s):  
User Experience ◽  
Dead Reckoning ◽  
Positioning System ◽  
Map Matching ◽  
Positioning Error ◽  
Positioning Accuracy ◽  
Integrated Method ◽  
Pedestrian Navigation ◽  
Wireless Positioning ◽  
Pedestrian Dead Reckoning

In order to solve the discontinuity of navigation and positioning in indoor signal coverage blind areas, and false region judgment caused by positioning error, an integrated method combining Wireless Positioning System (WPS), Pedestrian Dead Reckoning (PDR) and Map Matching (MM) is presented in this paper. By using the combination of Kalman filtered WPS and PDR information, inertial information and geographic information, pedestrian position could be evaluated. Through experiment, this method effectively increased positioning accuracy of the system as well as greatly improved the user experience.

scholarly journals Enhanced Heuristic Drift Elimination with Adaptive Zero-Velocity Detection and Heading Correction Algorithms for Pedestrian Navigation

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 951 ◽  
Author(s):  
Ruihui Zhu ◽  
Yunjia Wang ◽  
Baoguo Yu ◽  
Xingli Gan ◽  
Haonan Jia ◽  
...  
Keyword(s):  
Inertial Sensors ◽  
Angular Rate ◽  
Dead Reckoning ◽  
Detection Accuracy ◽  
Phase Detection ◽  
Pedestrian Navigation ◽  
Zero Velocity ◽  
Accumulated Error ◽  
Pedestrian Dead Reckoning ◽  
Dominant Direction

As pedestrian dead-reckoning (PDR), based on foot-mounted inertial sensors, suffers from accumulated error in velocity and heading, an improved heuristic drift elimination (iHDE) with a zero-velocity update (ZUPT) algorithm was proposed for simultaneously reducing the error in heading and velocity in complex paths, i.e., with pathways oriented at 45°, curved corridors, and wide areas. However, the iHDE algorithm does not consider the changes in pedestrian movement modes, and it can deteriorate when a pedestrian walks along a straight path without a pre-defined dominant direction. To solve these two problems, we propose enhanced heuristic drift elimination (eHDE) with an adaptive zero-velocity update (AZUPT) algorithm and novel heading correction algorithm. The relationships between the magnitude peaks of the y-axis angular rate and the detection thresholds were established only using the readings of the three-axis accelerometer and the three-axis gyroscopic, and a mechanism for constructing temporary dominant directions in real time was introduced. Real experiments were performed and the results showed that the proposed algorithm can improve the still-phase detection accuracy of a pedestrian at different movement motions and outperforms the iHDE algorithm in complex paths with many straight features.

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 An Indoor Localization Method Based on the Combination of Indoor Map Information and Inertial Navigation with Cascade Filter

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yushuai Zhang ◽  
Jianxin Guo ◽  
Feng Wang ◽  
Rui Zhu ◽  
Liping Wang
Keyword(s):  
Indoor Localization ◽  
Inertial Navigation ◽  
Detection Algorithm ◽  
Position Estimation ◽  
Position Error ◽  
Indoor Navigation ◽  
Bayesian Filter ◽  
Pedestrian Navigation ◽  
Zero Velocity ◽  
Average Position

The specific objective of this study is to propose a low-cost indoor navigation framework with nonbasic equipment by combining inertial sensors and indoor map messages. The proposed pedestrian navigation framework consists of a lower filter and an upper filter. In the lower filter which is designed based on the Kalman filter, the adaptive zero velocity detection algorithm is used to detect the zero velocity interval at different motion speeds, and then, zero velocity update is applied to rectify the inertial navigation solutions’ errors. In the upper filter which is designed based on the nonrecursive Bayesian filter, the map matching method with nonrecursive Bayesian filter is adopted to fuse the map prior information and the lower filter estimation results to correct the errors of navigation. The position estimation presented in this study achieves an average position error of 0.53 m compared to the ZUPT-aided inertial navigation system (INS) method under different motion states. The proposed pedestrian navigation algorithm achieves an average position error of 0.54 m as compared to the ZUPT-aided INS method among the different tested distances. The proposed framework simplifies the indoor positioning system under multiple motion speed conditions by ensuring the accuracy and stability property. The effectiveness and accuracy of the proposed framework are experimentally verified in various real-world scenarios.

Indoor Self-Localization Using Multiple Magnetic Sensors

2019 ◽  
Vol 31 (2) ◽  
pp. 203-211 ◽  
Author(s):  
Isaku Nagai ◽  
Jun Sakai ◽  
Keigo Watanabe ◽  
◽  
Keyword(s):  
Measurement Error ◽  
Particle Filter ◽  
Optical Sensor ◽  
Magnetic Sensors ◽  
The Self ◽  
Experimental Results ◽  
Map Matching ◽  
Multiple Sensors ◽  
Positioning Device

This study proposes an indoor self-localization for the estimation of the position and posture of an instrument using multiple magnetic sensors. First, a magnetic map for the localization is efficiently created using multiple sensors and a local positioning device made from an optical sensor and a gyroscope. For the localization estimating trajectories, the measurement error of the local positioning is corrected by matching it with the magnetic map. Our instrument is composed of six magnetic sensors, and the description of the self-localization details is based on the framework of a particle filter. The experimental results show better indoor path trajectories compared with a raw trajectory without map matching. The accuracy of the instrument using various numbers of magnetic sensors for the estimation is also investigated.

scholarly journals Inertial/magnetic sensors based pedestrian dead reckoning by means of multi-sensor fusion

2018 ◽  
Vol 39 ◽  
pp. 108-119 ◽  
Author(s):  
Sen Qiu ◽  
Zhelong Wang ◽  
Hongyu Zhao ◽  
Kairong Qin ◽  
Zhenglin Li ◽  
...  
Keyword(s):  
Sensor Fusion ◽  
Dead Reckoning ◽  
Magnetic Sensors ◽  
Pedestrian Dead Reckoning

scholarly journals A New PDR Navigation Device for Challenging Urban Environments

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Miguel Ortiz ◽  
Mathieu De Sousa ◽  
Valerie Renaudin
Keyword(s):  
Inertial Sensors ◽  
Dead Reckoning ◽  
Urban Environments ◽  
Privacy By Design ◽  
Handheld Device ◽  
Location Data ◽  
Pedestrian Navigation ◽  
New Research ◽  
Pedestrian Dead Reckoning ◽  
Original Device

The motivations, the design, and some applications of the new Pedestrian Dead Reckoning (PDR) navigation device, ULISS (Ubiquitous Localization with Inertial Sensors and Satellites), are presented in this paper. It is an original device conceived to follow the European recommendation of privacy by design to protect location data which opens new research toward self-contained pedestrian navigation approaches. Its application is presented with an enhanced PDR algorithm to estimate pedestrian’s footpaths in an autonomous manner irrespective of the handheld device carrying mode: texting or swinging. An analysis of real-time coding issues toward a demonstrator is also conducted. Indoor experiments, conducted with 3 persons, give a 5.8% mean positioning error over the 3 km travelled distances.

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