scholarly journals Infrastructure-Free Indoor Pedestrian Tracking with Smartphone Acoustic-Based Enhancement

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2458 ◽  
Author(s):  
Chao Liu ◽  
Sining Jiang ◽  
Shuo Zhao ◽  
Zhongwen Guo
Keyword(s):  
Doppler Effect ◽  
Location Estimation ◽  
Location Based Services ◽  
Estimation Accuracy ◽  
Measurement Unit ◽  
Average Error ◽  
Pedestrian Tracking ◽  
Tracking Accuracy ◽  
Acoustic Frequency ◽  
Indoor Pedestrian Tracking

Indoor pedestrian tracking has been identified as a key technology for indoor location-based services such as emergency locating, advertising, and gaming. However, existing smartphone-based approaches to pedestrian tracking in indoor environments have various limitations including a high cost of infrastructure constructing, labor-intensive fingerprint collection, and a vulnerability to moving obstacles. Moreover, our empirical study reveals that the accuracy of indoor locations estimated by a smartphone Inertial Measurement Unit (IMU) decreases severely when the pedestrian is arbitrarily wandering with an unstable speed. To improve the indoor tracking performance by enhancing the location estimation accuracy, we exploit smartphone-based acoustic techniques and propose an infrastructure-free indoor pedestrian tracking approach, called iIPT. The novelty of iIPT lies in the pedestrian speed reliability metric, which characterizes the reliability of the pedestrian speed provided by the smartphone IMU, and in a speed enhancing method, where we adjust a relatively less reliable pedestrian speed to the more reliable speed of a passing by “enhancer” based on the acoustic Doppler effect. iIPT thus changes the encountered pedestrians from an“obstacle” into an “enhancer.” Extensive real-world experiments in indoor scenarios have been conducted to verify the feasibility of realizing the acoustic Doppler effect between smartphones and to identify the applicable acoustic frequency range and transmission distance while reducing battery consumption. The experiment results demonstrate that iIPT can largely improve the tracking accuracy and decrease the average error compared with a conventional IMU-based method.

scholarly journals A Novel Indoor Positioning System Using Kernel Local Discriminant Analysis in Internet-of-Things

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Sajida Imran ◽  
Young-Bae Ko
Keyword(s):  
Feature Extraction ◽  
Discriminant Analysis ◽  
Indoor Positioning ◽  
Location Estimation ◽  
Location Based Services ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Positioning System ◽  
Local Fisher Discriminant Analysis ◽  
Indoor Positioning System

WLAN based localization is a key technique of location-based services (LBS) indoors. However, the indoor environment is complex; received signal strength (RSS) is highly uncertain, multimodal, and nonlinear. The traditional location estimation methods fail to provide fair estimation accuracy under the said environment. We proposed a novel indoor positioning system that considers the nonlinear discriminative feature extraction of RSS using kernel local Fisher discriminant analysis (KLFDA). KLFDA extracts location features in a well-preserved kernelized space. In the new kernel featured space, nonlinear RSS features are characterized effectively. Along with handling of nonlinearity, KLFDA also copes well with the multimodality in the RSS data. By performing KLFDA, the discriminating information contained in RSS is reorganized and maximally extracted. Prior to feature extraction, we performed outlier detection on RSS data to remove any anomalies present in the data. Experimental results show that the proposed approach obtains higher positioning accuracy by extracting maximal discriminate location features and discarding outlying information present in the RSS data.

scholarly journals Artificial Neural Network Approach to Mobile Location Estimation in GSM Network

2017 ◽  
Vol 63 (1) ◽  
pp. 39-44 ◽  
Author(s):  
Longinus S. Ezema ◽  
Cosmas I. Ani
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Performance Metrics ◽  
Federal Communication Commission ◽  
Location Estimation ◽  
Location Based Services ◽  
Estimation Accuracy ◽  
Neural Network Approach ◽  
Mobile Location ◽  
Gsm Network

AbstractThe increase in utilisation of mobile location-based services for commercial, safety and security purposes among others are the key drivers for improving location estimation accuracy to better serve those purposes. This paper proposes the application of Levenberg Marquardt training algorithm on new robust multilayered perceptron neural network architecture for mobile positioning fitting for the urban area in the considered GSM network using received signal strength (RSS). The key performance metrics such as accuracy, cost, reliability and coverage are the major points considered in this paper. The technique was evaluated using real data from field measurement and the results obtained proved the proposed model provides a practical positioning that meet Federal Communication Commission (FCC) accuracy requirement.

scholarly journals Comparison of Three Location Estimation Methods of an Autonomous Driving Robot for Underground Mines

2020 ◽  
Vol 10 (14) ◽  
pp. 4831
Author(s):  
Heonmoo Kim ◽  
Yosoon Choi
Keyword(s):  
Autonomous Robots ◽  
Estimation Error ◽  
Autonomous Driving ◽  
Underground Mine ◽  
Location Estimation ◽  
Underground Mines ◽  
Estimation Methods ◽  
Measurement Unit ◽  
Average Error ◽  
Average Estimation Error

In this study, we compared the accuracy of three location estimation methods of an autonomous driving robot for underground mines: an inertial measurement unit with encoder (IMU + encoder) sensors, Light Detecting and Ranging with encoder (LiDAR + encoder) sensors, and IMU with LiDAR and encoder (IMU + LiDAR + encoder) sensors. An accuracy comparison experiment was conducted in an indoor laboratory composed of four sections (X-change, X-Y change, X-Z change, and Y-change sections) that simulated an underground mine. The robot’s location was estimated using each of the three location estimation methods as the autonomous driving robot moved, and the results accuracy was analyzed by comparing the estimated location with the robot’s actual location. From the results of the indoor experiments, the average estimation error of the IMU + LiDAR + encoder sensors was approximately 0.09 m, that of the IMU + encoder was 0.19 m, and that of the LiDAR + encoder was 0.81 m. In a field experiment, the average error of the IMU + LiDAR + encoder was approximately 0.11 m, that of the IMU + encoder was 0.17 m, and that of the LiDAR + encoder was 0.70 m. In conclusion, the IMU + LiDAR + encoder method, which uses three types of sensors, showed the highest accuracy in estimating the location of autonomous robots in an underground mine.

scholarly journals Foot-Mounted Pedestrian Navigation Method by Comparing ADR and Modified ZUPT Based on MEMS IMU Array

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3787
Author(s):  
Li Xing ◽  
Xiaowei Tu ◽  
Zhi Chen
Keyword(s):  
Step Length ◽  
Mean Value ◽  
Measurement Unit ◽  
Reference Trajectory ◽  
Pedestrian Tracking ◽  
Tracking Accuracy ◽  
Pedestrian Navigation ◽  
The One ◽  
Foot Position ◽  
Mems Imu

Using an MEMS Inertial Measurement Unit (MEMS IMU) array mounted on foot is a feasible approach to improve the pedestrian tracking accuracy for the pedestrian navigation system (PNS). Based on the in-house developed IMU array, the paper proposes a new integrated framework that combines adaptive deck reckoning (ADR) with the modified zero velocity update (ZUPT). In the proposed ADR algorithm, the IMUs with large drift errors on the array are selected and removed according to the step length and the track angle computed by each IMU on the array. Then, by using the step length and the track angle of each step computed by remaining IMUs, the foot position extracted from the traditional ZUPT model is estimated on the basis of least squares (LS) so as to improve the traveled distance calculation accuracy. Compared with the traditional IMU array fusion method based on a maximum likelihood estimator (MLE) when it is used in the PNS, which is approximately taking the mean value of array readings, the proposed method is equivalent to adaptively fusing the array readings and thus improves the pedestrian tracking accuracy. To compare the proposed method with MLE, two different types of walking tracks are designed. The 161 m straight line experiments show that the end position by ADR/modified ZUPT method is much closer to the one of the reference trajectory compared with the MLE in repeated walks, and the closed-loop tracks about 300 m show that the positioning error with respect to the total traveled distance is less than 0.6% (1σ), which is higher than 1% (1σ) of MLE.

scholarly journals Vehicle Sideslip Angle Estimation Using Two Single-Antenna GPS Receivers

2010 ◽  
Author(s):  
Jong-Hwa Yoon ◽  
Huei Peng
Keyword(s):  
Low Cost ◽  
Longitudinal Velocity ◽  
Stability Control ◽  
Estimation Accuracy ◽  
Measurement Unit ◽  
Sideslip Angle ◽  
Gps Receivers ◽  
Single Antenna ◽  
Active Safety Systems ◽  
Safety Systems

Knowing vehicle sideslip angle accurately is critical for active safety systems such as Electronic Stability Control (ESC). Vehicle sideslip angle can be measured through optical speed sensors, or dual-antenna GPS. These measurement systems are costly (∼$5k to $100k), which prohibits wide adoption of such systems. This paper demonstrates that the vehicle sideslip angle can be estimated in real-time by using two low-cost single-antenna GPS receivers. Fast sampled signals from an Inertial Measurement Unit (IMU) compensate for the slow update rate of the GPS receivers through an Extended Kalman Filter (EKF). Bias errors of the IMU measurements are estimated through an EKF to improve the sideslip estimation accuracy. A key challenge of the proposed method lies in the synchronization of the two GPS receivers, which is achieved through an extrapolated update method. Analysis reveals that the estimation accuracy of the proposed method relies mainly on vehicle yaw rate and longitudinal velocity. Experimental results confirm the feasibility of the proposed method.

WTrack: HMM-based walk pattern recognition and indoor pedestrian tracking using phone inertial sensors

2014 ◽  
Vol 18 (8) ◽  
pp. 1901-1915 ◽  
Author(s):  
Xiaoguang Niu ◽  
Meng Li ◽  
Xiaohui Cui ◽  
Jin Liu ◽  
Shubo Liu ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Inertial Sensors ◽  
Pedestrian Tracking ◽  
Indoor Pedestrian Tracking

Robustness in Fingerprinting-Based Indoor Positioning Systems

2018 ◽  
pp. 88-141 ◽  
Author(s):  
Shih-Hau Fang
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Signal Strength ◽  
Histogram Equalization ◽  
Indoor Positioning ◽  
Received Signal Strength ◽  
Location Estimation ◽  
Location Based Services ◽  
Indoor Environments ◽  
Positioning Systems

Indoor positioning systems have received increasing attention for supporting location-based services in indoor environments. Received signal strength (RSS), mostly utilized in Wi-Fi fingerprinting systems, is known to be unreliable due to two reasons: orientation mismatch and variations in hardware. This chapter introduces an approach based on histogram equalization to compensate for orientation mismatch in robust Wi-Fi localization. The proposed method involves converting the temporal-spatial radio signal strength into a reference function (i.e., equalizing the histogram). This chapter also introduces an enhanced positioning feature, which is called delta-fused principal strength, to enhance the robustness of Wi-Fi localization against the problem of heterogeneous hardware. This algorithm computes the pairwise delta RSS and then integrates with RSS using principal component analysis. The proposed methods effectively and efficiently improve the robustness of location estimation in the presence of mismatch orientation and hardware variations, respectively.

Economic and Synergistic Pedestrian Tracking System with Service Cooperation for Indoor Environments

2011 ◽  
Vol 2 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Tomoya Ishikawa ◽  
Masakatsu Kourogi ◽  
Takeshi Kurata
Keyword(s):  
Indoor Environment ◽  
Tracking System ◽  
Tracking Performance ◽  
Quality Of Services ◽  
Rfid Tags ◽  
Indoor Environments ◽  
Pedestrian Tracking ◽  
Security Services ◽  
Indoor Pedestrian Tracking

This paper describes an indoor pedestrian tracking system that can economically improve the tracking performance and the quality and value of services by incorporating other services synergistically. The tracking system obtains position, orientation, and action of pedestrians continuously and accurately in large indoor environments by utilizing surveillance cameras and active RFID tags for security services and 3-D environment models for navigation services. Considering service cooperation and co-creative intelligence cycles, this system can improve both the tracking performance and the quality of services without significant increase of costs by sharing the existing infrastructures and the 3-D models among services. The authors conducted an evaluation of the tracking system in a large indoor environment and confirmed that the accuracy of the system can be improved by utilizing the infrastructures and the 3-D models. Synergistic services utilizing the tracking system and service cooperation can also enhance the quality and value of services.

scholarly journals Ski Jumping Trajectory Reconstruction Using Wearable Sensors via Extended Rauch-Tung-Striebel Smoother with State Constraints

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1995
Author(s):  
Xiang Fang ◽  
Benedikt Grüter ◽  
Patrick Piprek ◽  
Veronica Bessone ◽  
Johannes Petrat ◽  
...  
Keyword(s):  
State Constraints ◽  
Wearable Sensors ◽  
Measurement Data ◽  
Velocity Estimation ◽  
Estimation Accuracy ◽  
Measurement Unit ◽  
Shape Information ◽  
Video Recordings ◽  
Ski Jumping ◽  
Increasing Demand

To satisfy an increasing demand to reconstruct an athlete’s motion for performance analysis, this paper proposes a new method for reconstructing the position and velocity in the context of ski jumping trajectories. Therefore, state-of-the-art wearable sensors, including an inertial measurement unit, a magnetometer, and a GPS logger are used. The method employs an extended Rauch-Tung-Striebel smoother with state constraints to estimate state information offline from recorded raw measurements. In comparison to the classic inertial navigation system and GPS integration solution, the proposed method includes additional geometric shape information of the ski jumping hill, which are modeled as soft constraints and embedded into the estimation framework to improve the position and velocity estimation accuracy. Results for both simulated measurement data and real measurement data demonstrate the effectiveness of the proposed method. Moreover, a comparison between jump lengths obtained from the proposed method and video recordings shows the relative root-mean-square error of the reconstructed jump length is below 1.5 m depicting the accuracy of the algorithm.

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