Robust Adaptive Extended Kalman Filtering for Smart Phone-based Pedestrian Dead Reckoning Systems

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.

Download Full-text

Robust Adaptive Extended Kalman Filtering for Real Time MR-Thermometry Guided HIFU Interventions

IEEE Transactions on Medical Imaging ◽

10.1109/tmi.2011.2171772 ◽

2012 ◽

Vol 31 (3) ◽

pp. 533-542 ◽

Cited By ~ 26

Author(s):

S. Roujol ◽

B. D. de Senneville ◽

S. Hey ◽

C. Moonen ◽

M. Ries

Keyword(s):

Real Time ◽

Kalman Filtering ◽

Mr Thermometry ◽

Extended Kalman Filtering ◽

Robust Adaptive

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

S-PDR: SBAUPT-Based Pedestrian Dead Reckoning Algorithm for Free-Moving Handheld Devices

Geomatics ◽

10.3390/geomatics1020010 ◽

2021 ◽

Vol 1 (2) ◽

pp. 148-176

Author(s):

Maan Khedr ◽

Naser El-Sheimy

Keyword(s):

Smart Cities ◽

Dead Reckoning ◽

Human Motion ◽

Indoor Navigation ◽

Location Based Services ◽

Navigation Systems ◽

Short Term ◽

Navigation Solution ◽

Correction Technique ◽

Pedestrian Dead Reckoning

Mobile location-based services (MLBS) are attracting attention for their potential public and personal use for a variety of applications such as location-based advertisement, smart shopping, smart cities, health applications, emergency response, and even gaming. Many of these applications rely on Inertial Navigation Systems (INS) due to the degraded GNSS services indoors. INS-based MLBS using smartphones is hindered by the quality of the MEMS sensors provided in smartphones which suffer from high noise and errors resulting in high drift in the navigation solution rapidly. Pedestrian dead reckoning (PDR) is an INS-based navigation technique that exploits human motion to reduce navigation solution errors, but the errors cannot be eliminated without aid from other techniques. The purpose of this study is to enhance and extend the short-term reliability of PDR systems for smartphones as a standalone system through an enhanced step detection algorithm, a periodic attitude correction technique, and a novel PCA-based motion direction estimation technique. Testing shows that the developed system (S-PDR) provides a reliable short-term navigation solution with a final positioning error that is up to 6 m after 3 min runtime. These results were compared to a PDR solution using an Xsens IMU which is known to be a high grade MEMS IMU and was found to be worse than S-PDR. The findings show that S-PDR can be used to aid GNSS in challenging environments and can be a viable option for short-term indoor navigation until aiding is provided by alternative means. Furthermore, the extended reliable solution of S-PDR can help reduce the operational complexity of aiding navigation systems such as RF-based indoor navigation and magnetic map matching as it reduces the frequency by which these aiding techniques are required and applied.

Download Full-text