scholarly journals Heading Estimation for Pedestrian Dead Reckoning Based on Robust Adaptive Kalman Filtering

Sensors ◽  
2018 ◽  
Vol 18 (6) ◽  
pp. 1970 ◽  
Author(s):  
Dongjin Wu ◽  
Linyuan Xia ◽  
Jijun Geng
Keyword(s):  
Kalman Filtering ◽  
Dead Reckoning ◽  
Robust Adaptive ◽  
Heading Estimation ◽  
Pedestrian Dead Reckoning

scholarly journals Improved Pedestrian Dead Reckoning Based on a Robust Adaptive Kalman Filter for Indoor Inertial Location System

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 294 ◽  
Author(s):  
Qigao Fan ◽  
Hai Zhang ◽  
Peng Pan ◽  
Xiangpeng Zhuang ◽  
Jie Jia ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Inertial Sensors ◽  
Dead Reckoning ◽  
Estimation Algorithm ◽  
Measurement Unit ◽  
Threshold Method ◽  
Adaptive Kalman Filter ◽  
Robust Adaptive ◽  
Heading Estimation ◽  
Pedestrian Dead Reckoning

Pedestrian dead reckoning (PDR) systems based on a microelectromechanical-inertial measurement unit (MEMS-IMU) providing advantages of full autonomy and strong anti-jamming performance are becoming a feasible choice for pedestrian indoor positioning. In order to realize the accurate positioning of pedestrians in a closed environment, an improved pedestrian dead reckoning algorithm, mainly including improved step estimation and heading estimation, is proposed in this paper. Firstly, the original signal is preprocessed using the wavelet denoising algorithm. Then, the multi-threshold method is proposed to ameliorate the step estimation algorithm. For heading estimation suffering from accumulated error and outliers, robust adaptive Kalman filter (RAKF) algorithm is proposed in this paper, and combined with complementary filter to improve positioning accuracy. Finally, an experimental platform with inertial sensors as the core is constructed. Experimental results show that positioning error is less than 2.5% of the total distance, which is ideal for accurate positioning of pedestrians in enclosed environment.

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 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 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 Deep heading estimation for pedestrian dead reckoning

2020 ◽  
Vol 1656 ◽  
pp. 012009
Author(s):  
Jiayi Lin ◽  
Chengming Zou ◽  
Long Lan ◽  
Shanzhi Gu ◽  
Xinshang An
Keyword(s):  
Dead Reckoning ◽  
Heading Estimation ◽  
Pedestrian Dead Reckoning

scholarly journals Drift Control of Pedestrian Dead Reckoning (PDR) for Long Period Navigation under Different Smartphone Poses

2021 ◽  
Vol 10 (1) ◽  
pp. 21
Author(s):  
Ahmed Mansour ◽  
Wu Chen ◽  
Huan Luo ◽  
Yaxin Li ◽  
Jingxian Wang ◽  
...  
Keyword(s):  
Inertial Sensors ◽  
Low Cost ◽  
Dead Reckoning ◽  
Field Measurements ◽  
Step Length ◽  
Detection Algorithm ◽  
Long Period ◽  
Walking Pattern ◽  
Heading Estimation ◽  
Pedestrian Dead Reckoning

The inherent errors of low-cost inertial sensors cause significant heading drift that accumulates over time, making it difficult to rely on Pedestrian Dead Reckoning (PDR) for navigation over a long period. Moreover, the flexible portability of the smartphone poses a challenge to PDR, especially for heading determination. In this work, we aimed to control the PDR drift under the conditions of the unconstrained smartphone to eventually enhance the PDR performance. To this end, we developed a robust step detection algorithm that efficiently captures the peak and valley events of the triggered steps regardless of the device’s pose. The correlation between these events was then leveraged as distinct features to improve smartphone pose detection. The proposed PDR system was then designed to select the step length and heading estimation approach based on a real-time walking pattern and pose discrimination algorithm. We also leveraged quasi-static magnetic field measurements that have less disturbance for estimating reliable compass heading and calibrating the gyro heading. Additionally, we also calibrated the step length and heading when a straight walking pattern is observed between two base nodes. Our results showed improved device pose recognition accuracy. Furthermore, robust and accurate results were achieved for step length, heading and position during long-term navigation under unconstrained smartphone conditions.

scholarly journals Axis-Exchanged Compensation and Gait Parameters Analysis for High Accuracy Indoor Pedestrian Dead Reckoning

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Honghui Zhang ◽  
Jinyi Zhang ◽  
Duo Zhou ◽  
Wei Wang ◽  
Jianyu Li ◽  
...  
Keyword(s):  
Dead Reckoning ◽  
Step Length ◽  
Walking Distance ◽  
Analysis Algorithm ◽  
Indoor Location ◽  
Length Estimation ◽  
Gait Parameters ◽  
Parameters Analysis ◽  
Heading Estimation ◽  
Pedestrian Dead Reckoning

Pedestrian dead reckoning (PDR) is an effective way for navigation coupled with GNSS (Global Navigation Satellite System) or weak GNSS signal environment like indoor scenario. However, indoor location with an accuracy of 1 to 2 meters determined by PDR based on MEMS-IMU is still very challenging. For one thing, heading estimation is an important problem in PDR because of the singularities. For another thing, walking distance estimation is also a critical problem for pedestrian walking with randomness. Based on the above two problems, this paper proposed axis-exchanged compensation and gait parameters analysis algorithm to improve the navigation accuracy. In detail, an axis-exchanged compensation factored quaternion algorithm is put forward first to overcome the singularities in heading estimation without increasing the amount of computation. Besides, real-time heading is updated by R-adaptive Kalman filter. Moreover, gait parameters analysis algorithm can be divided into two steps: cadence detection and step length estimation. Thus, a method of cadence classification and interval symmetry is proposed to detect the cadence accurately. Furthermore, a step length model adjusted by cadence is established for step length estimation. Compared to the traditional PDR navigation, experimental results showed that the error of navigation reduces 32.6%.

scholarly journals Adaptive Kalman filtering-based pedestrian navigation algorithm for smartphones

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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