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 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.

Self-adaptive Filters for the Integration of Navigation Data

1983 ◽  
Vol 36 (1) ◽  
pp. 64-73 ◽  
Author(s):  
J. P. Abbott ◽  
C. R. Gent
Keyword(s):  
Kalman Filter ◽  
Measurement Errors ◽  
Internal Model ◽  
Adaptive Filters ◽  
Dead Reckoning ◽  
Internal Variables ◽  
Adaptive Kalman Filter ◽  
True Error ◽  
Navigation Data ◽  
Error Characteristics

The traditional non-adaptive Kalman filter includes models of the error characteristics of the navigation aids in use and such filters are very successful, so long as their model assumptions approximate to the true error characteristics sufficiently closely. However, for any filter there will be times when the environment changes and one or several aids will have errors which are not consistent with the assumed error models. It is necessary to consider carefully the sensitivity of the filter to such changes and, where a significant reduction in performance ensues, modifications to the filter are necessary.This paper introduces a Kalman filter which monitors the behaviour of internal variables to detect and characterize any model imperfections. The filter will then adapt its internal model of the environment accordingly. The discussion is restricted to the development of a navigation filter for integrating dead reckoning (EM log and gyrocompass) and Omega data. The principles are the same for any filter and details regarding similar analysis involving the use of other aids, for example Satnav and Decca, have been developed in a similar way.Before implementing any filter it is necessary to understand the behaviour of the measurement errors. For the dead reckoning and Omega aids this behaviour is described in section 2, while section 3 outlines a filter for integrating these aids and introduces the problems of model imperfections.

scholarly journals LLIO: Lightweight Learned Inertial Odometer

2021 ◽  
Author(s):  
Yan Wang ◽  
Jian Kuang ◽  
xiaoji niu
Keyword(s):  
Mobile Devices ◽  
Ground Plane ◽  
Dead Reckoning ◽  
Position Estimation ◽  
Data Driven ◽  
Trajectory Estimation ◽  
Motion Patterns ◽  
Similar Accuracy ◽  
Low Drift ◽  
Pedestrian Dead Reckoning

<div><div>The 3D position estimation of pedestrians is a vital module to build the connections between persons and things.</div><div>The traditional gait model-based methods cannot fulfill the various motion patterns.</div><div>And the various data-driven-based inertial odometry solutions focus on the 2D trajectory estimation on the ground plane, which is not suitable for AR applications.</div><div>TLIO (Tight Learned Inertial Odometry) proposed an inertial-based 3D motion estimator that achieves very low position drift by using the raw IMU measurements and the displacement predict coming from a neural network to provide low drift pedestrian dead reckoning.</div><div>However, TLIO is unsuitable for mobile devices because it is computationally expensive.</div><div>In this paper, a lightweight learned inertial odometry network (LLIO-Net) is designed for mobile devices.</div><div>By replacing the network in TLIO with the LLIO-Net, the proposed system shows similar accuracy but significantly improved efficiency.</div><div>Specifically, the proposed LLIO algorithm was implemented on mobile devices and compared the efficiency with TLIO.</div><div>The inference efficiency of the proposed system is 2-12 times that of TLIO.</div></div>

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