A Double-Step Unscented Kalman Filter and HMM-Based Zero-Velocity Update for Pedestrian Dead Reckoning Using MEMS Sensors

In smartphone indoor positioning, owing to the strong complementarity between pedestrian dead reckoning (PDR) and WiFi, a hybrid fusion scheme of them is drawing more and more attention. However, the outlier of WiFi will easily degrade the performance of the scheme, to remove them, many researches have been proposed such as: improving the WiFi individually or enhancing the scheme. Nevertheless, due to the inherent received signal strength (RSS) variation, there still exist some unremoved outliers. To solve this problem, this paper proposes the first outlier detection and removal strategy with the aid of Machine Learning (ML), so called WiFi-AGNES (Agglomerative Nesting), based on the extracted positioning characteristics of WiFi when the pedestrian is static. Then, the paper proposes the second outlier detection and removal strategy, so called WiFi-Chain, based on the extracted positioning characteristics of WiFi, PDR, and their complementary characteristics when the pedestrian is walking. Finally, a hybrid fusion scheme is proposed, which integrates the two proposed strategies, WiFi, PDR with an inertial-navigation-system-based (INS-based) attitude heading reference system (AHRS) via Extended Kalman Filter (EKF), and an Unscented Kalman Filter (UKF). The experiment results show that the two proposed strategies are effective and robust. With WiFi-AGNES, the minimum percentage of the maximum error (MaxE) is reduced by 66.5%; with WiFi-Chain, the MaxE of WiFi is less than 4.3 m; further the proposed scheme achieves the best performance, where the root mean square error (RMSE) is 1.43 m. Moreover, since characteristics are universal, the proposed scheme integrated the two characteristic-based strategies also possesses strong robustness.

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Dynamic Compensation for Environmental Magnetic Interference in Pedestrian Dead Reckoning Using MEMS Sensors

10.23919/ccc52363.2021.9549922 ◽

2021 ◽

Author(s):

Genlei Wang ◽

Senlin Zhang ◽

Meiqin Liu

Keyword(s):

Dead Reckoning ◽

Mems Sensors ◽

Dynamic Compensation ◽

Pedestrian Dead Reckoning

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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.

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Enhanced PDR-BLE Compensation Mechanism Based on HMM and AWCLA for Improving Indoor Localization

Sensors ◽

10.3390/s21216972 ◽

2021 ◽

Vol 21 (21) ◽

pp. 6972

Author(s):

Harun Jamil ◽

Faiza Qayyum ◽

Faisal Jamil ◽

Do-Hyeun Kim

Keyword(s):

Kalman Filter ◽

Indoor Environment ◽

Indoor Localization ◽

Unscented Kalman Filter ◽

Sensor Data ◽

Compensation Mechanism ◽

Localization Algorithm ◽

High Position ◽

Zero Velocity ◽

Conflicting Information

This paper presents an enhanced PDR-BLE compensation mechanism for improving indoor localization, which is considerably resilient against variant uncertainties. The proposed method of ePDR-BLE compensation mechanism (EPBCM) takes advantage of the non-requirement of linearization of the system around its current state in an unscented Kalman filter (UKF) and Kalman filter (KF) in smoothing of received signal strength indicator (RSSI) values. In this paper, a fusion of conflicting information and the activity detection approach of an object in an indoor environment contemplates varying magnitude of accelerometer values based on the hidden Markov model (HMM). On the estimated orientation, the proposed approach remunerates the inadvertent body acceleration and magnetic distortion sensor data. Moreover, EPBCM can precisely calculate the velocity and position by reducing the position drift, which gives rise to a fault in zero-velocity and heading error. The developed EPBCM localization algorithm using Bluetooth low energy beacons (BLE) was applied and analyzed in an indoor environment. The experiments conducted in an indoor scenario shows the results of various activities performed by the object and achieves better orientation estimation, zero velocity measurements, and high position accuracy than other methods in the literature.

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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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Threshold-less Zero-Velocity Detection Algorithm for Pedestrian Dead Reckoning

2019 European Navigation Conference (ENC) ◽

10.1109/euronav.2019.8714170 ◽

2019 ◽

Cited By ~ 2

Author(s):

Seong Yun Cho ◽

Chan Gook Park

Keyword(s):

Dead Reckoning ◽

Detection Algorithm ◽

Zero Velocity ◽

Pedestrian Dead Reckoning

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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.

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