Finding a Parked Car Location in a Multi-Storey Building without GPS Service

<p class="0abstract">This paper introduces a new approach named ‘Find My Parked Car’ to provide the user with the return way for his parked car location in the indoor garage of a multi-storey building where GPS is denied using the acquisition data obtained from the inertial sensors on smart devices such as smartphones. We build up a system that employs Pedestrian Dead-Reckoning Technique (PDR) to track the user inside the building. This approach harness the environment landmarks for floors separation and the PDR paths’ landmarks to enhance them by applying the Kalman Filter. Then we prunes the paths to remove the redundant parts of paths. Finally, we display the paths that guide the user back to his car via an android application. The feature of our approach is to help users returning back to parked car location based only on the available ubiquitous sensors in the new smartphones without any need to have any map or any additional infrastructure support.</p>

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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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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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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.

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A Robust Pedestrian Dead Reckoning System Using Low-Cost Magnetic and Inertial Sensors

IEEE Transactions on Instrumentation and Measurement ◽

10.1109/tim.2018.2869262 ◽

2019 ◽

Vol 68 (8) ◽

pp. 2996-3003 ◽

Cited By ~ 8

Author(s):

Ling-Feng Shi ◽

Yu-Le Zhao ◽

Gong-Xu Liu ◽

Sen Chen ◽

Yue Wang ◽

...

Keyword(s):

Inertial Sensors ◽

Low Cost ◽

Dead Reckoning ◽

Pedestrian Dead Reckoning

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Multimode Pedestrian Dead Reckoning Gait Detection Algorithm Based on Identification of Pedestrian Phone Carrying Position

Mobile Information Systems ◽

10.1155/2019/4709501 ◽

2019 ◽

Vol 2019 ◽

pp. 1-14 ◽

Cited By ~ 1

Author(s):

Ying Guo ◽

Qinghua Liu ◽

Xianlei Ji ◽

Shengli Wang ◽

Mingyang Feng ◽

...

Keyword(s):

Inertial Sensors ◽

Distance Estimation ◽

Dead Reckoning ◽

Principal Component ◽

Detection Algorithm ◽

Estimation Accuracy ◽

Indoor Environments ◽

Using Data ◽

The Time Domain ◽

Pedestrian Dead Reckoning

Pedestrian dead reckoning (PDR) is an essential technology for positioning and navigation in complex indoor environments. In the process of PDR positioning and navigation using mobile phones, gait information acquired by inertial sensors under various carrying positions differs from noise contained in the heading information, resulting in excessive gait detection deviation and greatly reducing the positioning accuracy of PDR. Using data from mobile phone accelerometer and gyroscope signals, this paper examined various phone carrying positions and switching positions as the research objective and analysed the time domain characteristics of the three-axis accelerometer and gyroscope signals. A principal component analysis algorithm was used to reduce the dimension of the extracted multidimensional gait feature, and the extracted features were random forest modelled to distinguish the phone carrying positions. The results show that the step detection and distance estimation accuracy in the gait detection process greatly improved after recognition of the phone carrying position, which enhanced the robustness of the PDR algorithm.

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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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