A method of heading estimation for pedestrian navigation based on information compression MDL criteria

The progress in the micro electro mechanical system (MEMS) sensors technology in size, cost, weight, and power consumption allows for new research opportunities in the navigation field. Today, most of smartphones, tablets, and other handheld devices are fully packed with the required sensors for any navigation system such as GPS, gyroscope, accelerometer, magnetometer, and pressure sensors. For seamless navigation, the sensors’ signal quality and the sensors availability are major challenges. Heading estimation is a fundamental challenge in the GPS-denied environments; therefore, targeting accurate attitude estimation is considered significant contribution to the overall navigation error. For that end, this research targets an improved pedestrian navigation by developing sensors fusion technique to exploit the gyroscope, magnetometer, and accelerometer data for device attitude estimation in the different environments based on quaternion mechanization. Results indicate that the improvement in the traveled distance and the heading estimations is capable of reducing the overall position error to be less than 15 m in the harsh environments.

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Using Constraints for Shoe Mounted Indoor Pedestrian Navigation

Journal of Navigation ◽

10.1017/s0373463311000518 ◽

2011 ◽

Vol 65 (1) ◽

pp. 15-28 ◽

Cited By ~ 34

Author(s):

Khairi Abdulrahim ◽

Chris Hide ◽

Terry Moore ◽

Chris Hill

Keyword(s):

Kalman Filter ◽

Average Distance ◽

Low Cost ◽

Navigation Systems ◽

Pedestrian Navigation ◽

Zero Velocity ◽

Average Return ◽

Measurement Units ◽

Heading Estimation ◽

Mems Imu

Shoe mounted Inertial Measurement Units (IMU) are often used for indoor pedestrian navigation systems. The presence of a zero velocity condition during the stance phase enables Zero Velocity Updates (ZUPT) to be applied regularly every time the user takes a step. Most of the velocity and attitude errors can be estimated using ZUPTs. However, good heading estimation for such a system remains a challenge. This is due to the poor observability of heading error for a low cost Micro-Electro-Mechanical (MEMS) IMU, even with the use of ZUPTs in a Kalman filter. In this paper, the same approach is adopted where a MEMS IMU is mounted on a shoe, but with additional constraints applied. The three constraints proposed herein are used to generate measurement updates for a Kalman filter, known as ‘Heading Update’, ‘Zero Integrated Heading Rate Update’ and ‘Height Update’.The first constraint involves restricting heading drift in a typical building where the user is walking. Due to the fact that typical buildings are rectangular in shape, an assumption is made that most walking in this environment is constrained to only follow one of the four main headings of the building. A second constraint is further used to restrict heading drift during a non-walking situation. This is carried out because the first constraint cannot be applied when the user is stationary. Finally, the third constraint is applied to limit the error growth in height. An assumption is made that the height changes in indoor buildings are only caused when the user walks up and down a staircase. Several trials were shown to demonstrate the effectiveness of integrating these constraints for indoor pedestrian navigation. The results show that an average return position error of 4·62 meters is obtained for an average distance of 1557 meters using only a low cost MEMS IMU.

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Heading Estimation for Indoor Pedestrian Navigation Using a Smartphone in the Pocket

Sensors ◽

10.3390/s150921518 ◽

2015 ◽

Vol 15 (9) ◽

pp. 21518-21536 ◽

Cited By ~ 55

Author(s):

Zhi-An Deng ◽

Guofeng Wang ◽

Ying Hu ◽

Di Wu

Keyword(s):

Pedestrian Navigation ◽

Heading Estimation

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Robust Heading Estimation for Indoor Pedestrian Navigation Using Unconstrained Smartphones

Wireless Communications and Mobile Computing ◽

10.1155/2018/5607036 ◽

2018 ◽

Vol 2018 ◽

pp. 1-11 ◽

Cited By ~ 3

Author(s):

Zhian Deng ◽

Xin Liu ◽

Zhiyu Qu ◽

Changbo Hou ◽

Weijian Si

Keyword(s):

Inertial Sensors ◽

Body Motion ◽

Hand Movements ◽

Estimation Errors ◽

Daily Lives ◽

Filtering Algorithm ◽

Pedestrian Navigation ◽

Heading Estimation ◽

Estimation System ◽

Previous User

Heading estimation using inertial sensors built-in smartphones has been considered as a central problem for indoor pedestrian navigation. For practical daily lives, it is necessary for heading estimation to allow an unconstrained use of smartphones, which means the varying device carrying positions and orientations. As a result, three special human body motion states, namely, random hand movements, carrying position transitions, and user turns, are introduced. However, most existing heading estimation approaches neglect the three motion states, which may render large estimation errors. We propose a robust heading estimation system adapting to the unconstrained use of smartphones. A novel detection and classification method is developed to detect the three motion states timely and discriminate them accurately. For normal working, the user heading is estimated by a PCA-based approach. If a user turn occurs, it is estimated by adding horizontal heading change to previous user heading directly. If one of the other two motion states occurs, it is obtained by averaging estimation results of the adjacent normal walking steps. Finally, an outlier filtering algorithm is developed to smooth the estimation results. Experimental results show that our approach is capable of handling the unconstrained situation of smartphones and outperforms previous approaches in terms of accuracy and applicability.

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Adaptive Kalman filtering-based pedestrian navigation algorithm for smartphones

International Journal of Advanced Robotic Systems ◽

10.1177/1729881420930934 ◽

2020 ◽

Vol 17 (3) ◽

pp. 172988142093093

Author(s):

Chen Yu ◽

Luo Haiyong ◽

Zhao Fang ◽

Wang Qu ◽

Shao Wenhua

Keyword(s):

Dead Reckoning ◽

Estimation Algorithm ◽

Error Model ◽

Smart Devices ◽

Pedestrian Navigation ◽

Navigation Algorithm ◽

Filter Process ◽

Heading Estimation ◽

The Impact ◽

Pedestrian Dead Reckoning

Pedestrian navigation with daily smart devices has become a vital issue over the past few years and the accurate heading estimation plays an essential role in it. Compared to the pedestrian dead reckoning (PDR) based solutions, this article constructs a scalable error model based on the inertial navigation system and proposes an adaptive heading estimation algorithm with a novel method of relative static magnetic field detection. To mitigate the impact of magnetic fluctuation, the proposed algorithm applies a two-way Kalman filter process. Firstly, it achieves the historical states with the optimal smoothing algorithm. Secondly, it adjusts the noise parameters adaptively to reestimate current attitudes. Different from the pedestrian dead reckoning-based solution, the error model system in this article contains more state information, which means it is more sensitive and scalable. Moreover, several experiments were conducted, and the experimental results demonstrate that the proposed heading estimation algorithm obtains better performance than previous approaches and our system outperforms the PDR system in terms of flexibility and accuracy.

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