HILL: A Hybrid Indoor Localization Scheme

2014 ◽  
Author(s):  
Sahil Anang Kharidia ◽  
Qiang Ye ◽  
Srinivas Sampalli ◽  
Jie Cheng ◽  
Hongwei Du ◽  
...  
Keyword(s):  
Indoor Localization ◽  
Localization Scheme

scholarly journals High-Accuracy Robot Indoor Localization Scheme Based on Robot Operating System Using Visible Light Positioning

2020 ◽  
Vol 12 (2) ◽  
pp. 1-16 ◽  
Author(s):  
Weipeng Guan ◽  
Shihuan Chen ◽  
Shangsheng Wen ◽  
Zequn Tan ◽  
Hongzhan Song ◽  
...  
Keyword(s):  
Operating System ◽  
Visible Light ◽  
Indoor Localization ◽  
High Accuracy ◽  
Robot Operating System ◽  
Localization Scheme

scholarly journals Time-Series Laplacian Semi-Supervised Learning for Indoor Localization †

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3867 ◽  
Author(s):  
Jaehyun Yoo
Keyword(s):  
Time Series ◽  
Supervised Learning ◽  
Indoor Localization ◽  
Learning Algorithm ◽  
Unlabeled Data ◽  
Training Data ◽  
Practical Implementation ◽  
Additional Information ◽  
Localization Scheme ◽  
The Impact

Machine learning-based indoor localization used to suffer from the collection, construction, and maintenance of labeled training databases for practical implementation. Semi-supervised learning methods have been developed as efficient indoor localization methods to reduce use of labeled training data. To boost the efficiency and the accuracy of indoor localization, this paper proposes a new time-series semi-supervised learning algorithm. The key aspect of the developed method, which distinguishes it from conventional semi-supervised algorithms, is the use of unlabeled data. The learning algorithm finds spatio-temporal relationships in the unlabeled data, and pseudolabels are generated to compensate for the lack of labeled training data. In the next step, another balancing-optimization learning algorithm learns a positioning model. The proposed method is evaluated for estimating the location of a smartphone user by using a Wi-Fi received signal strength indicator (RSSI) measurement. The experimental results show that the developed learning algorithm outperforms some existing semi-supervised algorithms according to the variation of the number of training data and access points. Also, the proposed method is discussed in terms of why it gives better performance, by the analysis of the impact of the learning parameters. Moreover, the extended localization scheme in conjunction with a particle filter is executed to include additional information, such as a floor plan.

Accurate Dead Reckoning Localization System Using RFID Phase

2014 ◽  
Vol 513-517 ◽  
pp. 3296-3299 ◽  
Author(s):  
Bo Li ◽  
Dong Wang
Keyword(s):  
Radio Frequency Identification ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Dead Reckoning ◽  
Location Based Service ◽  
Rfid Tag ◽  
Whole Process ◽  
Localization Scheme ◽  
Frequency Identification ◽  
Series Of Experiments

Nowadays, the demands of Location-based Service are growing fast. It contains huge business opportunities. This paper presents an efficient indoor localization scheme using Radio-Frequency Identification technology. The major idea of our method is Dead Reckoning, a method of navigation that using the best estimates of speed and direction to calculate users' motion trace. We implemented Dead Reckoning in indoor environment by taking advantage of features of RFID. We collected RFID tag phase value to calculate the velocity of users and recalibrate users' position by using known fixed RFID reader. We designed a series of experiments to verify the feasibility of our velocity calculation method, then we simulated the whole process of our system. The results show that our system can track user's motion effectively in indoor environment. We believe this is an encouraging result, holding promise for real-world deployment.

scholarly journals Support Vector Regression for Mobile Target Localization in Indoor Environments

Sensors ◽  
2022 ◽  
Vol 22 (1) ◽  
pp. 358
Author(s):  
Satish R. Jondhale ◽  
Vijay Mohan ◽  
Bharat Bhushan Sharma ◽  
Jaime Lloret ◽  
Shashikant V. Athawale
Keyword(s):  
Support Vector Regression ◽  
Target Location ◽  
Indoor Localization ◽  
Target Localization ◽  
Superior Performance ◽  
Support Vector ◽  
Generalized Regression Neural Network ◽  
Indoor Environments ◽  
Localization Scheme ◽  
Mobile Target

Trilateration-based target localization using received signal strength (RSS) in a wireless sensor network (WSN) generally yields inaccurate location estimates due to high fluctuations in RSS measurements in indoor environments. Improving the localization accuracy in RSS-based systems has long been the focus of a substantial amount of research. This paper proposes two range-free algorithms based on RSS measurements, namely support vector regression (SVR) and SVR + Kalman filter (KF). Unlike trilateration, the proposed SVR-based localization scheme can directly estimate target locations using field measurements without relying on the computation of distances. Unlike other state-of-the-art localization and tracking (L&T) schemes such as the generalized regression neural network (GRNN), SVR localization architecture needs only three RSS measurements to locate a mobile target. Furthermore, the SVR based localization scheme was fused with a KF in order to gain further refinement in target location estimates. Rigorous simulations were carried out to test the localization efficacy of the proposed algorithms for noisy radio frequency (RF) channels and a dynamic target motion model. Benefiting from the good generalization ability of SVR, simulation results showed that the presented SVR-based localization algorithms demonstrate superior performance compared to trilateration- and GRNN-based localization schemes in terms of indoor localization performance.

scholarly journals Photography Trilateration Indoor Localization with Image Sensor Communication

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3290 ◽  
Author(s):  
Nam Tuan Le ◽  
Yeong Min Jang
Keyword(s):  
Indoor Localization ◽  
Distance Estimation ◽  
Channel Modeling ◽  
Image Sensor ◽  
Error Rates ◽  
Localization Scheme ◽  
Wide Range ◽  
Successful Approach ◽  
Smart Mobile ◽  
The Internet Of Things

Localization has become an important aspect in a wide range of mobile services with the integration of the Internet of things and service on demand. Numerous mechanisms have been proposed for localization, most of which are based on the estimation of distances. Depending on the channel modeling, each mechanism has its advantages and limitations on deployment, exhibiting different performances in terms of error rates and implementation. With the development of technology, these limitations are rapidly overcome with hybrid systems and enhancement schemes. The successful approach depends on the achievement of a low error rate and its controllability by the integration of deployed products. In this study, we propose and analyze a new distance estimation technique employing photography and image sensor communications, also named optical camera communications (OCC). It represents one of the most important steps in the implemented trilateration localization scheme with real architectures and conditions of deployment which is the second our contribution for this article. With the advantages of the image sensor hardware integration in smart mobile devices, this technology has great potential in localization-based optical wireless communication

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