Pedestrian dead reckoning with attitude estimation using a fuzzy logic tuned adaptive kalman filter

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.

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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.

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Robust Adaptive Filter for Small Satellite Attitude Estimation Based on Magnetometer and Gyro

Abstract and Applied Analysis ◽

10.1155/2014/159149 ◽

2014 ◽

Vol 2014 ◽

pp. 1-7 ◽

Cited By ~ 4

Author(s):

Zhankui Zeng ◽

Shijie Zhang ◽

Yanjun Xing ◽

Xibin Cao

Keyword(s):

Kalman Filter ◽

Attitude Estimation ◽

Optimal Filtering ◽

System State ◽

Small Satellite ◽

Adaptive Kalman Filter ◽

Satellite Attitude ◽

Observational Noise ◽

Robust Adaptive ◽

The Impact

Based on magnetometer and gyro measurement, a sequential scheme is proposed to determine the orbit and attitude of small satellite simultaneously. In order to reduce the impact of orbital errors on attitude estimation, a robust adaptive Kalman filter is developed. It uses a scale factor and an adaptive factor, which are constructed by Huber function and innovation sequence, respectively, to adjust the covariance matrix of system state and observational noise, change the weights of predicted and measured parameters, get suitable Kalman filter gain and approximate optimal filtering results. Numerical simulations are carried out and the proposed filter is approved to be robust for the noise disturbance and parameter uncertainty and can provide higher accuracy attitude estimation.

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Attitude estimation using a Neuro-Fuzzy tuning based adaptive Kalman filter

Journal of Intelligent & Fuzzy Systems ◽

10.3233/ifs-141183 ◽

2015 ◽

Vol 29 (2) ◽

pp. 479-488 ◽

Cited By ~ 8

Author(s):

Mariana N. Ibarra-Bonilla ◽

P. Jorge Escamilla-Ambrosio ◽

Juan Manuel Ramirez-Cortes

Keyword(s):

Kalman Filter ◽

Attitude Estimation ◽

Adaptive Kalman Filter ◽

Neuro Fuzzy

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Smartphone-Based Pedestrian Dead Reckoning for 3D Indoor Positioning

Sensors ◽

10.3390/s21248180 ◽

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.

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