CSI-based Indoor Localization Error Bound Considering Pedestrian Motion

2020 ◽  
Author(s):  
Zhenya Zhang ◽  
Liangbo Xie ◽  
Mu Zhou ◽  
Yong Wang
Keyword(s):  
Error Bound ◽  
Indoor Localization ◽  
Localization Error

scholarly journals Application of Deep Convolutional Neural Networks and Smartphone Sensors for Indoor Localization

2019 ◽  
Vol 9 (11) ◽  
pp. 2337 ◽  
Author(s):  
Imran Ashraf ◽  
Soojung Hur ◽  
Yongwan Park
Keyword(s):  
Magnetic Field ◽  
Nearest Neighbor ◽  
Indoor Localization ◽  
Scene Recognition ◽  
Localization Error ◽  
K Nearest Neighbor ◽  
Current Location ◽  
Smartphone Sensors ◽  
The Impact ◽  
Localization Systems

Indoor localization systems are susceptible to higher errors and do not meet the current standards of indoor localization. Moreover, the performance of such approaches is limited by device dependence. The use of Wi-Fi makes the localization process vulnerable to dynamic factors and energy hungry. A multi-sensor fusion based indoor localization approach is proposed to overcome these issues. The proposed approach predicts pedestrians’ current location with smartphone sensors data alone. The proposed approach aims at mitigating the impact of device dependency on the localization accuracy and lowering the localization error in the magnetic field based localization systems. We trained a deep learning based convolutional neural network to recognize the indoor scene which helps to lower the localization error. The recognized scene is used to identify a specific floor and narrow the search space. The database built of magnetic field patterns helps to lower the device dependence. A modified K nearest neighbor (mKNN) is presented to calculate the pedestrian’s current location. The data from pedestrian dead reckoning further refines this location and an extended Kalman filter is implemented to this end. The performance of the proposed approach is tested with experiments on Galaxy S8 and LG G6 smartphones. The experimental results demonstrate that the proposed approach can achieve an accuracy of 1.04 m at 50 percent, regardless of the smartphone used for localization. The proposed mKNN outperforms K nearest neighbor approach, and mean, variance, and maximum errors are lower than those of KNN. Moreover, the proposed approach does not use Wi-Fi for localization and is more energy efficient than those of Wi-Fi based approaches. Experiments reveal that localization without scene recognition leads to higher errors.

scholarly journals Possibilities of Using Kalman Filters in Indoor Localization

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1564
Author(s):  
Katerina Fronckova ◽  
Pavel Prazak
Keyword(s):  
Object Tracking ◽  
Stochastic Models ◽  
Kalman Filters ◽  
Indoor Environment ◽  
Moving Objects ◽  
Indoor Localization ◽  
Low Energy ◽  
Localization Error ◽  
Energy Data ◽  
The Mean

Kalman filters are a set of algorithms based on the idea of a filter described by Rudolf Emil Kalman in 1960. Kalman filters are used in various application domains, including localization, object tracking, and navigation. The text provides an overview and discussion of the possibilities of using Kalman filters in indoor localization. The problems of static localization and localization of dynamically moving objects are investigated, and corresponding stochastic models are created. Three algorithms for static localization and one algorithm for dynamic localization are described and demonstrated. All algorithms are implemented in the MATLAB software, and then their performance is tested on Bluetooth Low Energy data from a real indoor environment. The results show that by using Kalman filters, the mean localization error of two meters can be achieved, which is one meter less than in the case of using the standard fingerprinting technique. In general, the presented principles of Kalman filters are applicable in connection with various technologies and data of various nature.

An Information-Theoretic View of WLAN Localization Error Bound in GPS-Denied Environment

2019 ◽  
Vol 68 (4) ◽  
pp. 4089-4093 ◽  
Author(s):  
Mu Zhou ◽  
Yanmeng Wang ◽  
Yiyao Liu ◽  
Zengshan Tian
Keyword(s):  
Error Bound ◽  
Localization Error ◽  
Information Theoretic

CSI Ranging-based Wi-Fi Indoor Localization Error Analysis

2021 ◽  
Author(s):  
Yuexin Long ◽  
Mu Zhou ◽  
Zhenya Zhang ◽  
Wei Nie
Keyword(s):  
Error Analysis ◽  
Indoor Localization ◽  
Localization Error

scholarly journals Benefits and Impact of Joint Metric of AOA/RSS/TOF on Indoor Localization Error

2016 ◽  
Vol 6 (10) ◽  
pp. 296 ◽  
Author(s):  
Qing Jiang ◽  
Feng Qiu ◽  
Mu Zhou ◽  
Zengshan Tian
Keyword(s):  
Indoor Localization ◽  
Localization Error

scholarly journals Hybrid Indoor Localization Using IMU Sensors and Smartphone Camera

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5084 ◽  
Author(s):  
Alwin Poulose ◽  
Dong Seog Han
Keyword(s):  
Hybrid System ◽  
Indoor Localization ◽  
Simultaneous Localization And Mapping ◽  
Maximum Error ◽  
Measurement Unit ◽  
Localization Error ◽  
Positioning Systems ◽  
Localization System ◽  
Localization And Mapping ◽  
Localization Systems

Smartphone camera or inertial measurement unit (IMU) sensor-based systems can be independently used to provide accurate indoor positioning results. However, the accuracy of an IMU-based localization system depends on the magnitude of sensor errors that are caused by external electromagnetic noise or sensor drifts. Smartphone camera based positioning systems depend on the experimental floor map and the camera poses. The challenge in smartphone camera-based localization is that accuracy depends on the rapidness of changes in the user’s direction. In order to minimize the positioning errors in both the smartphone camera and IMU-based localization systems, we propose hybrid systems that combine both the camera-based and IMU sensor-based approaches for indoor localization. In this paper, an indoor experiment scenario is designed to analyse the performance of the IMU-based localization system, smartphone camera-based localization system and the proposed hybrid indoor localization system. The experiment results demonstrate the effectiveness of the proposed hybrid system and the results show that the proposed hybrid system exhibits significant position accuracy when compared to the IMU and smartphone camera-based localization systems. The performance of the proposed hybrid system is analysed in terms of average localization error and probability distributions of localization errors. The experiment results show that the proposed oriented fast rotated binary robust independent elementary features (BRIEF)-simultaneous localization and mapping (ORB-SLAM) with the IMU sensor hybrid system shows a mean localization error of 0.1398 m and the proposed simultaneous localization and mapping by fusion of keypoints and squared planar markers (UcoSLAM) with IMU sensor-based hybrid system has a 0.0690 m mean localization error and are compared with the individual localization systems in terms of mean error, maximum error, minimum error and standard deviation of error.

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