Adaptive Kalman filtering-based pedestrian navigation algorithm for smartphones

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.

Download Full-text

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

Sensors ◽

10.3390/s19020294 ◽

2019 ◽

Vol 19 (2) ◽

pp. 294 ◽

Cited By ~ 7

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.

Download Full-text

Gravity-Based Methods for Heading Computation in Pedestrian Dead Reckoning

Sensors ◽

10.3390/s19051170 ◽

2019 ◽

Vol 19 (5) ◽

pp. 1170 ◽

Cited By ~ 3

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.

Download Full-text

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

Sensors ◽

10.3390/s19214782 ◽

2019 ◽

Vol 19 (21) ◽

pp. 4782 ◽

Cited By ~ 1

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.

Download Full-text

An Improved in Stride Estimation Algorithm of Pedestrian Dead Reckoning

2017 9th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA) ◽

10.1109/icmtma.2017.0046 ◽

2017 ◽

Author(s):

Wei Beibei ◽

Chen Tao ◽

Zhou Zhao

Keyword(s):

Dead Reckoning ◽

Estimation Algorithm ◽

Pedestrian Dead Reckoning

Download Full-text

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.437.870 ◽

2013 ◽

Vol 437 ◽

pp. 870-875 ◽

Cited By ~ 1

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.

Download Full-text

Characterization of the impact of indoor Doppler errors on Pedestrian Dead Reckoning

Proceedings of the 2012 IEEE/ION Position, Location and Navigation Symposium ◽

10.1109/plans.2012.6236838 ◽

2012 ◽

Cited By ~ 3

Author(s):

Valerie Renaudin ◽

Zhe He ◽

Mark Petovello

Keyword(s):

Dead Reckoning ◽

The Impact ◽

Pedestrian Dead Reckoning

Download Full-text

A New PDR Navigation Device for Challenging Urban Environments

Journal of Sensors ◽

10.1155/2017/4080479 ◽

2017 ◽

Vol 2017 ◽

pp. 1-11 ◽

Cited By ~ 5

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.

Download Full-text

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

Applied Sciences ◽

10.3390/app9183727 ◽

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.

Download Full-text