A reliable and accurate indoor localization method using phone inertial sensors

2012 ◽  
Author(s):  
Fan Li ◽  
Chunshui Zhao ◽  
Guanzhong Ding ◽  
Jian Gong ◽  
Chenxing Liu ◽  
...  
Keyword(s):  
Indoor Localization ◽  
Inertial Sensors ◽  
Localization Method

scholarly journals APFiLoc: An Infrastructure-Free Indoor Localization Method Fusing Smartphone Inertial Sensors, Landmarks and Map Information

Sensors ◽  
2015 ◽  
Vol 15 (10) ◽  
pp. 27251-27272 ◽  
Author(s):  
Jianga Shang ◽  
Fuqiang Gu ◽  
Xuke Hu ◽  
Allison Kealy
Keyword(s):  
Indoor Localization ◽  
Inertial Sensors ◽  
Localization Method

An improved indoor localization method using smartphone inertial sensors

2013 ◽  
Author(s):  
Jiuchao Qian ◽  
Jiabin Ma ◽  
Rendong Ying ◽  
Peilin Liu ◽  
Ling Pei
Keyword(s):  
Indoor Localization ◽  
Inertial Sensors ◽  
Localization Method

A novel indoor localization method based on virtual AP estimation

2012 ◽  
Author(s):  
Yijun Mo ◽  
Yifeng Cai ◽  
Bang Wang
Keyword(s):  
Indoor Localization ◽  
Localization Method

WiFi-assisted multi-floor indoor localization with inertial sensors

2016 ◽  
Author(s):  
Yifan Li ◽  
Jianxin Chen ◽  
Ying Shi ◽  
Yi Cheng ◽  
Lei Wang
Keyword(s):  
Indoor Localization ◽  
Inertial Sensors

A robust indoor localization method with calibration strategy based on joint distribution adaptation

2021 ◽  
Author(s):  
Yujie Wang ◽  
Yi Lei ◽  
Yong Zhang ◽  
Lu Yao
Keyword(s):  
Joint Distribution ◽  
Indoor Localization ◽  
Localization Method

scholarly journals End-to-End Sequential Indoor Localization Using Smartphone Inertial Sensors and WiFi

2021 ◽  
Author(s):  
Mukhamet Nurpeiissov ◽  
Askat Kuzdeuov ◽  
Aslan Assylkhanov, ◽  
Yerbolat Khassanov ◽  
Hüseyin Atakan Varol
Keyword(s):  
Indoor Localization ◽  
Inertial Sensors ◽  
Sampling Rate ◽  
Reference Points ◽  
Measurement Unit ◽  
Localization System ◽  
Practical Applications ◽  
External Data ◽  
End To End ◽  
Commercial Off The Shelf

This paper addresses sequential indoor localization using WiFi and Inertial Measurement Unit (IMU) modules commonly found in commercial off-the-shelf smartphones. Specifically, we developed an end-to-end neural network-based localization system integrating WiFi received signal strength indicator (RSSI) and IMU data without external data fusion models. The developed system leverages the advantages of WiFi and IMU modules to locate finer-level sequential positions of a user at 150 Hz sampling rate. Additionally, to demonstrate the efficacy of the proposed approach, we created the IMUWiFine dataset comprising IMU and WiFi RSSI readings sequentially collected at fine-level reference points. The dataset contains 120 trajectories covering an aggregate distance of over 14 kilometers. We conducted extensive experiments using deep learning models and achieved a mean error distance of 1.1 meters on an unseen evaluation set, which makes our approach suitable for many practical applications requiring meter-level accuracy. To enable experiment and result reproducibility, we made the developed localization system and IMUWiFine dataset publicly available in our GitHub repository.<br>

Using RSSI Simple Localization Method to Implement the Context-Aware and Social Recommendation System

2013 ◽  
Vol 479-480 ◽  
pp. 1213-1217
Author(s):  
Mu Yen Chen ◽  
Ming Ni Wu ◽  
Hsien En Lin
Keyword(s):  
Social Media ◽  
Social Relations ◽  
Context Awareness ◽  
Recommendation System ◽  
Product Information ◽  
Indoor Localization ◽  
Context Aware ◽  
Social Recommendation ◽  
Localization Method ◽  
The Social

This study integrates the concept of context-awareness with association algorithms and social media to establish the Context-aware and Social Recommendation System (CASRS). The Simple RSSI Indoor Localization Module (SRILM) locates the user position; integrating SRILM with Apriori Recommendation Module (ARM) provides effective recommended product information. The Social Media Recommendation Module (SMRM) connects to users social relations, so that the effectiveness for users to gain product information is greatly enhanced. This study develops the system based on actual context.

scholarly journals A Novel Passive Indoor Localization Method by Fusion CSI Amplitude and Phase Information

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 875 ◽  
Author(s):  
Xiaochao Dang ◽  
Xiong Si ◽  
Zhanjun Hao ◽  
Yaning Huang
Keyword(s):  
Indoor Localization ◽  
Rapid Development ◽  
Location Based Services ◽  
Signal Interference ◽  
Indoor Environments ◽  
Phase Information ◽  
Localization Accuracy ◽  
Localization Method ◽  
Indoor Location ◽  
Conference Room

With the rapid development of wireless network technology, wireless passive indoor localization has become an increasingly important technique that is widely used in indoor location-based services. Channel state information (CSI) can provide more detailed and specific subcarrier information, which has gained the attention of researchers and has become an emphasis in indoor localization technology. However, existing research has generally adopted amplitude information for eigenvalue calculations. There are few research studies that have used phase information from CSI signals for localization purposes. To eliminate the signal interference existing in indoor environments, we present a passive human indoor localization method named FapFi, which fuses CSI amplitude and phase information to fully utilize richer signal characteristics to find location. In the offline stage, we filter out redundant values and outliers in the CSI amplitude information and then process the CSI phase information. A fusion method is utilized to store the processed amplitude and phase information as a fingerprint database. The experimental data from two typical laboratory and conference room environments were gathered and analyzed. The extensive experimental results demonstrate that the proposed algorithm is more efficient than other algorithms in data processing and achieves decimeter-level localization accuracy.

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