scholarly journals Implementation of Hybrid Indoor Positioning System based on Wi-Fi and PDR in Smartphone

2019 ◽  
Vol 8 (2S6) ◽  
pp. 357-361
Keyword(s):  
Kalman Filter ◽  
Inertial Sensors ◽  
Dead Reckoning ◽  
Indoor Positioning ◽  
Initial Position ◽  
Mobile User ◽  
Sensor Data ◽  
Positioning System ◽  
Fusion Algorithm ◽  
Positioning Accuracy

This paper proposed thehybridindoor positioning system in smartphone for positioning accuracy by fusion of wireless-fidelity (Wi-Fi) signals and inertial sensors from pedestrian dead reckoning (PDR) in smartphone. The proposed system uses Wi-Fi as the source of received signal strength indicator (RSSI) for fingerprint and smartphones sensor data from PDR. RSSI signals are used to determine the initial position and reduce error accumulation of PDR while smartphone sensor data are used to estimate user trajectory. Extended Kalman Filter (EKF) is the fusion algorithm used for its similarity with Kalman Filter (KF) but with advantages of processing non-linear progressions. An estimated 49 steps were detected which is identical to the 50 steps taken in the experiment while showing a trajectory similar to the actual route taken by the mobile user. A benefit of using built-in smartphone sensors is its cost-effectiveness and availability that does not require additional hardware. In addition, a nonlinear EKF is used to enhance the positioning accuracy in the proposed system. Further studies will be made in the potential of indoor positioning algorithm including the effect of noise interference on sensors and RSSI and the accumulated errors resulting from walking

scholarly journals INDOOR POSITIONING USING WLAN FINGERPRINT MATCHING AND PATH ASSESSMENT WITH RETROACTIVE ADJUSTMENT ON MOBILE DEVICES

2018 ◽  
Vol XLII-4 ◽  
pp. 253-260
Author(s):  
E. Gulo ◽  
G. Sohn ◽  
A. Afnan
Keyword(s):  
Mobile Devices ◽  
Inertial Sensor ◽  
Dead Reckoning ◽  
Indoor Positioning ◽  
Sensor Data ◽  
Fingerprint Matching ◽  
Positioning Accuracy ◽  
Viable Approach ◽  
Similar Accuracy ◽  
Pedestrian Dead Reckoning

<p><strong>Abstract.</strong> With the increasing number and usage of mobile devices in people’s daily life, indoor positioning has attracted a lot attention from both academia and industry for the purpose of providing location-aware services. This work proposes an indoor positioning system, primarily based on WLAN fingerprint matching, that includes various minor improvements to improve the positioning accuracy of the algorithm, as well as improve the quality and reduce the collection time of the reference fingerprints. In addition, a novel Path Evaluation and Retroactive Adjustment module is employed; it intends to improve the positioning accuracy of the system in a similar fashion to a Pedestrian Dead Reckoning implemented along with WLAN Fingerprint Matching in a Sensor Fusion system. The benefit of this approach being that it avoids the requirement of inertial sensor data, as well as its intensive computation and power use, while providing a similar accuracy improvement to Pedestrian Dead Reckoning. Our experimental results demonstrate that this may be a viable approach for positioning using mobile devices in an indoor environment.</p>

3D Indoor Positioning System Based on MEMS Sensors

2015 ◽  
Vol 645-646 ◽  
pp. 498-503
Author(s):  
Jin Lin Zhu ◽  
Bing Mo ◽  
Chao Dong Ling ◽  
Wen Yuan Fu
Keyword(s):  
Inertial Sensors ◽  
Human Movement ◽  
Indoor Positioning ◽  
Sensor Data ◽  
Positioning System ◽  
Mems Sensors ◽  
3D Space ◽  
Novel Method ◽  
Mine Accident ◽  
Indoor Positioning System

Nowadays, most of scholar study the 2D indoor positioning system based inertial sensors. But in some case, the information of 3D space about person is very important, such as mine accident, fire rescue, and so on. In this paper, we focus on designing and implementing a 3D indoor positioning system based on MEMS sensors and barometer sensor. The MEMS sensors including a three-axis accelerometer, a three-axis gyroscope, and a three-axis magnetometer is used for acquiring information of human movement. Barometer sensor is used to measure the height of the person where he is. Then the collected raw data is sent to a PC by a MCU wirelessly. Thus the performance of the 3D indoor positioning system is optimized by moving data post processing to a PC. In a 3D indoor positioning system, the integral drift and the heading drift are the main sources of the errors. Therefore, a method which is the combination of the zero velocity updates and the sensor data fusion based on an extended Kalman filter is proposed. Further improvement on the accuracy of positioning can be achieved by removing the static biases. A novel method including compensating the sensor biases based on experiment is presented. These methods have been carefully investigated through theoretical study and simulation. The simulation results indicate 3D indoor positioning is achievable through the application of the presented methods.

scholarly journals An integrity monitoring algorithm for WiFi/PDR/smartphone-integrated indoor positioning system based on unscented Kalman filter

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Haiyun Yao ◽  
Hong Shu ◽  
Hongxing Sun ◽  
B. G. Mousa ◽  
Zhenghang Jiao ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Computational Complexity ◽  
Unscented Kalman Filter ◽  
Indoor Positioning ◽  
Positioning System ◽  
Positioning Accuracy ◽  
Integrity Monitoring ◽  
Observation Vector ◽  
Indoor Positioning System ◽  
Monitoring Algorithm

AbstractIndoor positioning navigation technologies have developed rapidly, but little effort has been expended on integrity monitoring in Pedestrian Dead Reckoning (PDR) and WiFi indoor positioning navigation systems. PDR accuracy will drift over time. Meanwhile, WiFi positioning accuracy decreases in complex indoor environments due to severe multipath propagation and interference with signals when people move about. In our research, we aimed to improve positioning quality with an integrity monitoring algorithm for a WiFi/PDR-integrated indoor positioning system based on the unscented Kalman filter (UKF). The integrity monitoring is divided into three phases. A test statistic based on the innovation of UKF determines whether the positioning system is abnormal. Once a positioning system abnormality is detected, a robust UKF (RUKF) is triggered to achieve higher positioning accuracy. Again, the innovation of RUKF is used to judge the outliers in observations and identify positioning system faults. In the last integrity monitoring phase, users will be alerted in time to reduce the risk from positioning fault. We conducted a simulation to analyze the computational complexity of integrity monitoring. The results showed that it did not substantially increase the overall computational complexity when the number of dimensions in the state vector and observation vector in the system is small (< 20). In practice, the number of dimensions of state vector and observation vector in an indoor positioning system rarely exceeds 20. The proposed integrity monitoring algorithm was tested in two field experiments, showing that the proposed algorithm is quite robust, yielding higher positioning accuracy than the traditional method, using only UKF.

A Satellite and Inertial Navigation Solution in Crises Management Operation for First Responder Application

2016 ◽  
Vol 101 ◽  
pp. 158-167
Author(s):  
Antonio Ghetti ◽  
Luca Vittuari ◽  
Matteo Zanzi
Keyword(s):  
Inertial Sensors ◽  
Dead Reckoning ◽  
Human Motion ◽  
Sensor Data ◽  
Reference Trajectory ◽  
Fusion Algorithm ◽  
Definition Of ◽  
Field Test Result ◽  
Gnss Data ◽  
Crises Management

This work describes the activities and the results achieved within the SPARTACUS project with regards to the development of a pedestrian positioning system for first responders based on inertial sensors and GNSS data integration. In particular, three steps have been dealt with. At first the analysis of a typical human motion profile, highlighting the Zero Velocity Update constraints; secondly, the definition of a high accurate reference trajectory for test validation purposes, with both indoor and outdoor tracks. Finally, the tuning of Extended Kalman Filter parameters for the calibration of the best sensor data fusion algorithm mainly focused to dead reckoning positioning and field test result assessment.

scholarly journals Improved Visible Light-Based Indoor Positioning System Using Machine Learning Classification and Regression

2019 ◽  
Vol 9 (6) ◽  
pp. 1048 ◽  
Author(s):  
Huy Tran ◽  
Cheolkeun Ha
Keyword(s):  
Machine Learning ◽  
Visible Light ◽  
Noise Reduction ◽  
Indoor Positioning ◽  
Computational Time ◽  
Dual Function ◽  
Positioning System ◽  
Positioning Accuracy ◽  
Positioning Systems ◽  
Machine Learning Classification

Recently, indoor positioning systems have attracted a great deal of research attention, as they have a variety of applications in the fields of science and industry. In this study, we propose an innovative and easily implemented solution for indoor positioning. The solution is based on an indoor visible light positioning system and dual-function machine learning (ML) algorithms. Our solution increases positioning accuracy under the negative effect of multipath reflections and decreases the computational time for ML algorithms. Initially, we perform a noise reduction process to eliminate low-intensity reflective signals and minimize noise. Then, we divide the floor of the room into two separate areas using the ML classification function. This significantly reduces the computational time and partially improves the positioning accuracy of our system. Finally, the regression function of those ML algorithms is applied to predict the location of the optical receiver. By using extensive computer simulations, we have demonstrated that the execution time required by certain dual-function algorithms to determine indoor positioning is decreased after area division and noise reduction have been applied. In the best case, the proposed solution took 78.26% less time and provided a 52.55% improvement in positioning accuracy.

scholarly journals An Integrated Adaptive Kalman Filter for High-Speed UAVs

2019 ◽  
Vol 9 (9) ◽  
pp. 1916 ◽  
Author(s):  
Tiantian Huang ◽  
Hui Jiang ◽  
Zhuoyang Zou ◽  
Lingyun Ye ◽  
Kaichen Song
Keyword(s):  
Kalman Filter ◽  
High Speed ◽  
Sensor Data ◽  
Adaptive Kalman Filter ◽  
Fusion Algorithm ◽  
Multi Sensor Data Fusion ◽  
Aerial Vehicle ◽  
Robust Adaptive ◽  
Tracking Filter ◽  
Filter Algorithms

In order to solve the problems of filtering divergence and low accuracy in Kalman filter (KF) applications in a high-speed unmanned aerial vehicle (UAV), this paper proposed a new method of integrated robust adaptive Kalman filter: strong adaptive Kalman filter (SAKF). The simulation of two high-dynamic conditions and a practical experiment were designed to verify the new multi-sensor data fusion algorithm. Then the performance of the Sage–Husa adaptive Kalman filter (SHAKF), strong tracking filter (STF), H∞ filter and SAKF were compared. The results of the simulation and practical experiments show that the SAKF can automatically select its filtering process under different conditions, according to an anomaly criterion. SAKF combines the advantages of SHAKF, H∞ filter and STF, and has the characteristics of high accuracy, robustness and good tracking skill. The research has proved that SAKF is more appropriate in high-speed UAV navigation than single filter algorithms.

scholarly journals Mobile Robot Indoor Positioning System Based on K-ELM

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Haixia Wang ◽  
Junliang Li ◽  
Wei Cui ◽  
Xiao Lu ◽  
Zhiguo Zhang ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Indoor Localization ◽  
Indoor Positioning ◽  
Positioning System ◽  
Positioning Accuracy ◽  
Positioning Method ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine ◽  
Positioning Algorithm ◽  
Indoor Positioning System

Mobile Robot Indoor Positioning System has wide application in the industry and home automation field. Unfortunately, existing mobile robot indoor positioning methods often suffer from poor positioning accuracy, system instability, and need for extra installation efforts. In this paper, we propose a novel positioning system which applies the centralized positioning method into the mobile robot, in which real-time positioning is achieved via interactions between ARM and computer. We apply the Kernel extreme learning machine (K-ELM) algorithm as our positioning algorithm after comparing four different algorithms in simulation experiments. Real-world indoor localization experiments are conducted, and the results demonstrate that the proposed system can not only improve positioning accuracy but also greatly reduce the installation efforts since our system solely relies on Wi-Fi devices.

scholarly journals A Novel KGP Algorithm for Improving INS/GPS Integrated Navigation Positioning Accuracy

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1623 ◽  
Author(s):  
Huibing Zhang ◽  
Tong Li ◽  
Lihua Yin ◽  
Dingke Liu ◽  
Ya Zhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Kalman Filter ◽  
Decision Tree ◽  
Particle Swarm ◽  
Sensor Data ◽  
Gradient Boosting ◽  
Integrated Navigation ◽  
Swarm Optimization ◽  
Positioning Accuracy

The fusion of multi-source sensor data is an effective method for improving the accuracy of vehicle navigation. The generalization abilities of neural-network-based inertial devices and GPS integrated navigation systems weaken as the nonlinearity in the system increases, resulting in decreased positioning accuracy. Therefore, a KF-GDBT-PSO (Kalman Filter-Gradient Boosting Decision Tree-Particle Swarm Optimization, KGP) data fusion method was proposed in this work. This method establishes an Inertial Navigation System (INS) error compensation model by integrating Kalman Filter (KF) and Gradient Boosting Decision Tree (GBDT). To improve the prediction accuracy of the GBDT, we optimized the learning algorithm and the fitness parameter using Particle Swarm Optimization (PSO). When the GPS signal was stable, the KGP method was used to solve the nonlinearity issue between the vehicle feature and positioning data. When the GPS signal was unstable, the training model was used to correct the positioning error for the INS, thereby improving the positioning accuracy and continuity. The experimental results show that our method increased the positioning accuracy by 28.20–59.89% compared with the multi-layer perceptual neural network and random forest regression.

scholarly journals Leveraging Visual Place Recognition to Improve Indoor Positioning with Limited Availability of WiFi Scans

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3657 ◽  
Author(s):  
Michał R. Nowicki ◽  
Piotr Skrzypczyński
Keyword(s):  
Indoor Localization ◽  
Dead Reckoning ◽  
Indoor Positioning ◽  
Positioning System ◽  
Indoor Environments ◽  
University Campus ◽  
Sources Of Information ◽  
Visual Place Recognition ◽  
Indoor Positioning System

WiFi-based fingerprinting is promising for practical indoor localization with smartphones because this technique provides absolute estimates of the current position, while the WiFi infrastructure is ubiquitous in the majority of indoor environments. However, the application of WiFi fingerprinting for positioning requires pre-surveyed signal maps and is getting more restricted in the recent generation of smartphones due to changes in security policies. Therefore, we sought new sources of information that can be fused into the existing indoor positioning framework, helping users to pinpoint their position, even with a relatively low-quality, sparse WiFi signal map. In this paper, we demonstrate that such information can be derived from the recognition of camera images. We present a way of transforming qualitative information of image similarity into quantitative constraints that are then fused into the graph-based optimization framework for positioning together with typical pedestrian dead reckoning (PDR) and WiFi fingerprinting constraints. Performance of the improved indoor positioning system is evaluated on different user trajectories logged inside an office building at our University campus. The results demonstrate that introducing additional sensing modality into the positioning system makes it possible to increase accuracy and simultaneously reduce the dependence on the quality of the pre-surveyed WiFi map and the WiFi measurements at run-time.

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