Academic literature on the topic 'Wi-Fi Indoor positioning system'
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Journal articles on the topic "Wi-Fi Indoor positioning system"
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Poulose, Alwin, and Dong Seog Han. "Hybrid Deep Learning Model Based Indoor Positioning Using Wi-Fi RSSI Heat Maps for Autonomous Applications." Electronics 10, no. 1 (December 22, 2020): 2. http://dx.doi.org/10.3390/electronics10010002.
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Positioning using Wi-Fi received signal strength indication (RSSI) signals is an effective method for identifying the user positions in an indoor scenario. Wi-Fi RSSI signals in an autonomous system can be easily used for vehicle tracking in underground parking. In Wi-Fi RSSI signal based positioning, the positioning system estimates the signal strength of the access points (APs) to the receiver and identifies the user’s indoor positions. The existing Wi-Fi RSSI based positioning systems use raw RSSI signals obtained from APs and estimate the user positions. These raw RSSI signals can easily fluctuate and be interfered with by the indoor channel conditions. This signal interference in the indoor channel condition reduces localization performance of these existing Wi-Fi RSSI signal based positioning systems. To enhance their performance and reduce the positioning error, we propose a hybrid deep learning model (HDLM) based indoor positioning system. The proposed HDLM based positioning system uses RSSI heat maps instead of raw RSSI signals from APs. This results in better localization performance for Wi-Fi RSSI signal based positioning systems. When compared to the existing Wi-Fi RSSI based positioning technologies such as fingerprint, trilateration, and Wi-Fi fusion approaches, the proposed approach achieves reasonably better positioning results for indoor localization. The experiment results show that a combination of convolutional neural network and long short-term memory network (CNN-LSTM) used in the proposed HDLM outperforms other deep learning models and gives a smaller localization error than conventional Wi-Fi RSSI signal based localization approaches. From the experiment result analysis, the proposed system can be easily implemented for autonomous applications.
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Zhang, Wei, Xianghong Hua, Kegen Yu, Weining Qiu, Shoujian Zhang, and Xiaoxing He. "A novel WiFi indoor positioning strategy based on weighted squared Euclidean distance and local principal gradient direction." Sensor Review 39, no. 1 (January 21, 2019): 99–106. http://dx.doi.org/10.1108/sr-06-2017-0109.
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Purpose This paper aims to introduce the weighted squared Euclidean distance between points in signal space, to improve the performance of the Wi-Fi indoor positioning. Nowadays, the received signal strength-based Wi-Fi indoor positioning, a low-cost indoor positioning approach, has attracted a significant attention from both academia and industry. Design/methodology/approach The local principal gradient direction is introduced and used to define the weighting function and an average algorithm based on k-means algorithm is used to estimate the local principal gradient direction of each access point. Then, correlation distance is used in the new method to find the k nearest calibration points. The weighted squared Euclidean distance between the nearest calibration point and target point is calculated and used to estimate the position of target point. Findings Experiments are conducted and the results indicate that the proposed Wi-Fi indoor positioning approach considerably outperforms the weighted k nearest neighbor method. The new method also outperforms support vector regression and extreme learning machine algorithms in the absence of sufficient fingerprints. Research limitations/implications Weighted k nearest neighbor approach, support vector regression algorithm and extreme learning machine algorithm are the three classic strategies for location determination using Wi-Fi fingerprinting. However, weighted k nearest neighbor suffers from dramatic performance degradation in the presence of multipath signal attenuation and environmental changes. More fingerprints are required for support vector regression algorithm to ensure the desirable performance; and labeling Wi-Fi fingerprints is labor-intensive. The performance of extreme learning machine algorithm may not be stable. Practical implications The new weighted squared Euclidean distance-based Wi-Fi indoor positioning strategy can improve the performance of Wi-Fi indoor positioning system. Social implications The received signal strength-based effective Wi-Fi indoor positioning system can substitute for global positioning system that does not work indoors. This effective and low-cost positioning approach would be promising for many indoor-based location services. Originality/value A novel Wi-Fi indoor positioning strategy based on the weighted squared Euclidean distance is proposed in this paper to improve the performance of the Wi-Fi indoor positioning, and the local principal gradient direction is introduced and used to define the weighting function.
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Muroň, Mikuláš, and David Procházka. "Wi‑Fi Indoor Localisation: A Deeper Insight Into Patterns in the Fingerprint Map Data." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 66, no. 6 (2018): 1565–71. http://dx.doi.org/10.11118/actaun201866061565.
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Localisation via Wi‑Fi networks is one of the possible techniques which can be used for positioning inside buildings or in other places without the GPS signal. The accurate indoor positioning system can help users with localisation or navigation within unfamiliar places. Almost all buildings are covered with the Wi‑Fi signal. Using the currently existing infrastructure will minimise cost for construction other types of indoor positioning systems. Among other reasons, usage of Wi‑Fi for positioning is also convenient because almost every mobile device has a Wi‑Fi capability and therefore the system can be easily used by everyone. However, an important factor is the precision of such a solution. The article is focused on the evaluation of Wi‑Fi localisation precision within the university grounds.
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Cui, Wei, Qingde Liu, Linhan Zhang, Haixia Wang, Xiao Lu, and Junliang Li. "A robust mobile robot indoor positioning system based on Wi-Fi." International Journal of Advanced Robotic Systems 17, no. 1 (January 1, 2020): 172988141989666. http://dx.doi.org/10.1177/1729881419896660.
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Recently, most of the existing mobile robot indoor positioning systems (IPSs) use infrared sensors, cameras, and other extra infrastructures. They usually suffer from high cost and special hardware implementation. In order to address the above problems, this article proposes a Wi-Fi-based indoor mobile robot positioning system and designs and develops a robot positioning platform based on the commercial Wi-Fi devices, such as Wi-Fi routers. Furthermore, a robust principal component analysis-based extreme learning machine algorithm is proposed to address the issue of noisy measurements in IPSs. Real-world robot indoor positioning experiments are extensively carried out and the results verify the effectiveness and superiority of the proposed system.
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Lukito, Yuan. "Multi Layer Perceptron Model for Indoor Positioning System Based on Wi-Fi." Jurnal Teknologi dan Sistem Komputer 5, no. 3 (July 31, 2017): 123–28. http://dx.doi.org/10.14710/jtsiskom.5.3.2017.123-128.
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Indoor positioning system issue is an open problem that still needs some improvements. This research explores the utilization of multilayer perceptron in determining someone’s position inside a building or a room, which generally known as Indoor Positioning System. The research was conducted in some steps: dataset normalization, multilayer perceptron implementation, training process of multilayer perceptron, evaluation, and analysis. The training process has been conducted many times to find the best parameters that produce the best accuracy rate. The experiment produces 79,16% as the highest accuracy rate. Compared to previous research, this result is comparably lower and needs some parameters tweaking or changing the neural networks architectures.
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Haider, Amir, Yiqiao Wei, Shuzhi Liu, and Seung-Hoon Hwang. "Pre- and Post-Processing Algorithms with Deep Learning Classifier for Wi-Fi Fingerprint-Based Indoor Positioning." Electronics 8, no. 2 (February 8, 2019): 195. http://dx.doi.org/10.3390/electronics8020195.
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To accommodate the rapidly increasing demand for connected infrastructure, automation for industrial sites and building smart cities, the development of Internet of Things (IoT)-based solutions is considered one of the major trends in modern day industrial revolution. In particular, providing high precision indoor positioning services for such applications is a key challenge. Wi-Fi fingerprint-based indoor positioning systems have been adapted as promising candidates for such applications. The performance of such indoor positioning systems degrade drastically due to several impairments like noisy datasets, high variation in Wi-Fi signals over time, fading of Wi-Fi signals due to multipath propagation caused by hurdles, people walking in the area under consideration and the addition/removal of Wi-Fi access points (APs). In this paper, we propose data pre- and post-processing algorithms with deep learning classifiers for Wi-Fi fingerprint-based indoor positioning, in order to provide immunity against limitations in the database and the indoor environment. In addition, we investigate the performance of the proposed system through simulation as well as extensive experiments. The results demonstrate that the pre-processing algorithm can efficiently fill in the missing Wi-Fi received signal strength fingerprints in the database, resulting in a success rate of 88.96% in simulation and 86.61% in a real-time experiment. The post-processing algorithm can improve the results from 9.05–10.94% for the conducted experiments, providing the highest success rate of 95.94% with a precision of 4 m for Wi-Fi fingerprint-based indoor positioning.
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Ali, Muhammad, Soojung Hur, and Yongwan Park. "Wi-Fi-Based Effortless Indoor Positioning System Using IoT Sensors." Sensors 19, no. 7 (March 27, 2019): 1496. http://dx.doi.org/10.3390/s19071496.
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Wi-Fi positioning based on fingerprinting has been considered as the most widely used technology in the field of indoor positioning. The fingerprinting database has been used as an essential part of the Wi-Fi positioning system. However, the offline phase of the calibration involves a laborious task of site analysis which involves costs and a waste of time. We offer an indoor positioning system based on the automatic generation of radio maps of the indoor environment. The proposed system does not require any effort and uses Wi-Fi compatible Internet-of-Things (IoT) sensors. Propagation loss parameters are automatically estimated from the online feedback of deployed sensors and the radio maps are updated periodically without any physical intervention. The proposed system leverages the raster maps of an environment with the wall information only, against computationally extensive techniques based on vector maps that require precise information on the length and angles of each wall. Experimental results show that the proposed system has achieved an average accuracy of 2 m, which is comparable to the survey-based Wi-Fi fingerprinting technique.
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Yu, C., and N. El-Sheimy. "INDOOR MAP AIDED INS/WI-FI INTEGRATED LBS ON SMARTPHONE PLATFORMS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W4 (September 14, 2017): 425–29. http://dx.doi.org/10.5194/isprs-annals-iv-2-w4-425-2017.
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In this research, an indoor map aided INS/Wi-Fi integrated location based services (LBS) applications is proposed and implemented on smartphone platforms. Indoor map information together with measurements from an inertial measurement unit (IMU) and Received Signal Strength Indicator (RSSI) value from Wi-Fi are collected to obtain an accurate, continuous, and low-cost position solution. The main challenge of this research is to make effective use of various measurements that complement each other without increasing the computational burden of the system. The integrated system in this paper includes three modules: INS, Wi-Fi (if signal available) and indoor maps. A cascade structure Particle/Kalman filter framework is applied to combine the different modules. Firstly, INS position and Wi-Fi fingerprint position integrated through Kalman filter for estimating positioning information. Then, indoor map information is applied to correct the error of INS/Wi-Fi estimated position through particle filter. Indoor tests show that the proposed method can effectively reduce the accumulation positioning errors of stand-alone INS systems, and provide stable, continuous and reliable indoor location service.
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Leca, Cristian-Liviu, Ioan Nicolaescu, and Petrica Ciotirnae. "Crowdsensing Influences and Error Sources in Urban Outdoor Wi-Fi Fingerprinting Positioning." Sensors 20, no. 2 (January 12, 2020): 427. http://dx.doi.org/10.3390/s20020427.
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Wi-Fi fingerprinting positioning systems have been deployed for a long time in location-based services for indoor environments. Combining mobile crowdsensing and Wi-Fi fingerprinting systems could reduce the high cost of collecting the necessary data, enabling the deployment of the resulting system for outdoor positioning in areas with dense Wi-Fi coverage. In this paper, we present the results attained in the design and evaluation of an urban fingerprinting positioning system based on crowdsensed Wi-Fi measurements. We first assess the quality of the collected measurements, highlighting the influence of received signal strength on data collection. We then evaluate the proposed system by comparing the influence of the crowdsensed fingerprints on the overall positioning accuracy for different scenarios. This evaluation helps gain valuable insight into the design and deployment of urban Wi-Fi positioning systems while also allowing the proposed system to match GPS-like accuracy in similar conditions.
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Shin, Geon-Sik, and Yong-Hyeon Shin. "Wi-Fi Based Indoor Positioning System Using Hybrid Algorithm." Journal of Advanced Navigation Technology 19, no. 6 (December 30, 2015): 564–73. http://dx.doi.org/10.12673/jant.2015.19.6.564.
Full textDissertations / Theses on the topic "Wi-Fi Indoor positioning system"
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Tran, Huy Phuong. "Context-Aware Wi-Fi Infrastructure-based Indoor Positioning Systems." PDXScholar, 2019. https://pdxscholar.library.pdx.edu/open_access_etds/5009.
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Large enterprises are often interested in tracking objects and people within buildings to improve resource allocation and occupant experience. Infrastructure-based indoor positioning systems (IIPS) can provide this service at low-cost by leveraging already deployed Wi-Fi infrastructure. Typically, IIPS perform localization and tracking of devices by measuring only Wi-Fi signals at wireless access points and do not rely on inertial sensor data at mobile devices (e.g., smartphones), which would require explicit user consent and sensing capabilities of the devices.
Despite these advantages, building an economically viable cost-effective IIPS that can accurately and simultaneously track many devices over very large buildings is difficult due to three main challenges. First, Wi-Fi signal measurements are extremely noisy due to unpredictable multipath propagation and signal attenuation. Second, as the IIPS obtain measurements in a best effort manner without requiring any applications installed on a tracked device, the measurements are temporally sparse and non-periodic, which makes it difficult to exploit historical measurements. Third, the cost-effective IIPS have limited computational resources, in turn limiting scalability in terms of the number of simultaneously tracked devices.
Prior approaches have narrowly focused on either improving the accuracy or reducing the complexity of localization algorithms. To compute the location at the current time step, they typically use only the latest explicit Wi-Fi measurements (e.g., signal strengths). The novelty of our approach lies in considering contexts of a device that can provide useful indications of the device's location. One such example of context is device motion. It indicates whether or not the device's location has changed. For a stationary device, the IIPS can either skip expensive device localization or aggregate noisy, temporally sparse location estimates to improve localization accuracy. Another example of context applicable to a moving device is a floor map that consists of pre-defined path segments that a user can take. The map can be leveraged to constrain noisy, temporally sparse location estimates on the paths.
The thesis of this dissertation is that embedding context-aware capabilities in the IIPS enhances its performance in tracking many devices simultaneously and accurately. Specifically, we develop motion detection and map matching to show the benefits of leveraging two critical contexts: device motion and floor map. Providing motion detection and map matching is non-trivial in the IIPS where we must rely only on data from the Wi-Fi infrastructure.
This thesis makes two contributions. First, we develop feature-based and deep learning-based motion detection models that exploit temporal patterns in Wi-Fi measurements across different access points to classify device motion in real time. Our extensive evaluations on datasets from real Wi-Fi deployments show that our motion detection models can detect device motion accurately. This, in turn, allows the IIPS to skip repeated location computation for stationary devices or improve the accuracy of localizing these devices. Second, we develop graph-based and image-based map matching models to exploit floor maps. The novelty of the graph-based approach lies in applying geometric and topological constraints to select which path segment to align the current location estimate. Our graph-based map matching can align a location estimate of a user device on the path taken by the user and close to the user's current location. The novelty of the image-based approach lies in representing for the first time, input data including location estimates and the floor map as 2D images. This novel representation enables the design, development, and application of encoder-decoder neural networks to exploit spatial relationships in input images to potentially improve location accuracy. In our evaluation, we show that the image-based approach can improve location accuracy with large simulated datasets, compared to the graph-based approach. Together, these contributions enable improvement of the IIPS in its ability to accurately and simultaneously track many devices over large buildings.
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Zhang, Dezhi. "Towards an open global Wi-Fi indoor positioning system via implicit crowdsourcing." Thesis, University of Nottingham, 2017. http://eprints.nottingham.ac.uk/40130/.
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Location-based Services (LBSs) are important building blocks for a wide spectrum of context-aware applications. The Global Positioning System (GPS) has provided almost ubiquitous positioning services in outdoor environments and enabled many outdoor LBSs such as routing navigation, location check-in and field analysis. However, the indoor LBSs equivalents, e.g., routing the visually-impaired, are not yet pervasively available due to many unaddressed challenges in indoor positioning system deployments. The overarching goal of this project is to develop practical systems to address such challenges and finally build an Open Global Indoor Positioning System (OGIPS). OGIPS is a supplementation to GPS indoors and the combination of OGIPS and GPS is anticipated to provide truly global positioning services to virtually anyone. The Wi-Fi Positioning System (WPS) is a dominant enabling technology of OGIPS for its sheer prevalence in pervasiveness, reliability and performance. This doctoral study project identified three major challenges for building OGIPS based on WPSs and proposed corresponding solutions. The first challenge is how to deploy WPSs with zero cost. WPS deployment requires Radio Map calibration, which, in current industrial practice, relies on high-cost scene analysis. To reduce the deployment cost, it is possible to leverage the free Crowdsourced data. In fact, an enormous amount of Wi-Fi signal measurements could be collected by Implicit Crowdsourcing, that is, collecting unlabeled data in an unobtrusive manner during normal courses of the smartphone users, e.g., strolling around shopping malls. The challenges is then reformulated as how to perform Radio Map calibration via Implicit Crowdsourcing. This project formulates the targeted problem with a novel Hyper-Graph Matching framework, which lends various merits to the system in terms of scalability, extendability and robustness. The elegant problem formulation allows the system to exploit the accomplishments of Graph Matching researches in the past decades, especially in Computer vision. We designed, implemented the system, HyperLoc, and validated it with extensive experiments with both simulated and real-world data. Experimental results indicates that HyperLoc is able to construct zero-cost WPSs in real-world settings. The overall positioning performance of HyperLoc, a zero-cost system, is comparable to high-cost manually-calibrated WPSs . To our knowledge, HyperLoc is the first work to apply Graph Matching techniques to Radio Map calibrations and the first work that develops a practical and scalable zero-cost WPS implementation in real-world settings. The second challenge is how to maintain WPSs performance over time with zero cost. The Radio Map describes the signal environment in relation to the physical environment of a venue. However, the relation often changes substantially upon changes of radio propagation patterns, caused by many factors, e.g., change of the layout of the venue. To maintain consistent and reliable performance, Radio Maps must be versionized and re-calibrated. Here we arrive at the second challenge, that is, how to effectively manage Radio Map versioning with zero cost. This project proposed a novel Radio Map versioning control system, RAEDS, by detecting system anomalous events that degrade indoor positioning performance substantially. The system generalizes arbitrary Radio Map degrading factors as Radio Map anomalous events, which could be modeled and hence detected using state-of-the-art event detection techniques. We designed, implemented and evaluated RAEDS with both synthetic and real-world experiments. The results showed that RAEDS is able to detect anomalous events accurately with a low false alarm rate. To our knowledge, RAEDS is the first work to apply advanced event detection techniques in WPS health monitoring for system versioning control. The combination of HyperLoc and RAEDS is anticipated to enable a practical zero-cost WPS in real-world settings. However, many challenges still present. The OGIPS architecture shall be carefully designed to accommodate the domain-specific requirements of OGIPS in addition to the general requirements of highly-available and scalable systems. Hence the third challenge is how to architect OGIPS to meet the desired requirements. Practical design goals are discussed comprehensively and a proposed design of the architecture and implementations is described in details. Guidelines and recommendations in system implementations were made. More importantly, the proposed OGIPS design is orthogonal to HyperLoc and RAEDS. That means OGIPS is able to flexibly integrate other zero-cost WPSs implementations at high-level. This merit allows other researchers to reuse the proposed architecture with their proprietary zero-cost WPS implementations. The proposed solutions in this work are expected to pave the path towards building OGIPS in real-world settings. Future research efforts will be devoted to improving the adaptiveness and robustness of the proposed systems in terms of device heterogeneity, adaptiveness to user patterns and insufficient data. Finally, the findings of this thesis are expected to contribute to the research communities sharing the same conviction, that is, to make the indoor positioning service accessible to virtually anyone, anytime and anywhere.
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Hörnlund, Emil, and Rasmus Schenström. "Indoor Location Surveillance : Utilizing Wi-Fi and Bluetooth Signals." Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-17978.
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Personal information nowadays have become valuable for many stakeholders. We want to find out how much information someone can gather from our daily devices such as a smartphone, using some budget devices together with some programming knowledge. Can we gather enough information to be able to determine a location to a target device? The main objectives of our bachelor thesis is to determine the accuracy of positioning for nearby personal devices using trilateration of short-distance communications (Wi-Fi vs Bluetooth). But also, how much and what information our devices leak without us knowing with respect to personal integrity. We collected Wi-Fi and Bluetooth data in total from four target devices. Two different experiments were executed, calibration experiment and visualization experiment. The data were collected by capturing the Wi-Fi and Bluetooth Received Signal Strength Indication(RSSI) transmitted wirelessly from target devices. We then apply a method called trilateration to be able to pinpoint a target to a location. In theory, Bluetooth signals are twice as accurate as Wi-Fi signals. In practise, we were able to locate a target device with an accuracy of 5 - 10 meters. Bluetooth signals are stable but have long response time while Wi-Fi signals have short response time but high fluctuation in the RSSI values. The idea itself, being able to determine a handheld device position is not impossible, as can be seen from our results. It may though require more powerful hardware to secure an acceptable accuracy. On the other hand, achieving this kind of results from such a cheap hardware as Raspberry Pi:s are truly amazing.
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Rasch, Kevin, and Dennis Lipponen. "Inomhuspositionering med tre olika tekniker – Bluetooth Low Energy, Wi-Fi och GPS : En jämförelsestudie av positioneringsverktyg vid inomhuspositionering." Thesis, Högskolan Dalarna, Informatik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:du-27999.
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Olika navigeringstekniker är något som människan använder sig av dagligen. Det är och har varit en viktig del genom tiderna, i början handlade det om att kunna navigera till havs med hjälp av stjärnorna och idag har många människor i världen någon form av smarttelefon som kan användas som navigeringsverktyg. Inomhuspositionering är något som har blivit ett hett ämne den senaste tiden och eftersom GPS fungerar sämre inomhus har nya tekniker utvecklats för att navigera och positionera sig inomhus. De tekniker som ligger i framkant nu är bland annat Bluetooth Low Energy (BLE) och Wi-Fi där man med hjälp av triangulering kan positionera ut en användare inomhus. Frågan återstår då, vilket positioneringsverktyg är då lämpligt för inomhuspositionering. Detta examensarbete har utförts med en kombination av strategier. Som börjar med en jämförelsestudie av tre olika positioneringstekniker, BLE Wi-Fi och GPS, för att ta fram en lämplig positioneringsteknik för utvärdering i form av ett experiment där flera faktorer, främst precisionen, testas. I denna studie användes BLE beacons som positioneringsverktyg dels för att den var lätt att implementera men även för att den skulle uppnå hög precision. För att testa BLE beacon utvecklades en lätt applikationsprototyp som hade som huvudsyfte att kunna positionera ut en användare i ett koordinatsystem med hjälp av XYvärden. Genom att triangulering användes för att kunna positionera ut en användare uppnådde studien en medelmarginal på ungefär 76 centimeter när fem stycken beacons användes i ett område på 4 x 3 meter, vilket vi ansåg som godkänt för att kunna dra slutsatsen att BLE beacon är ett lämpligt verktyg när det kommer till en inomhuspositionering. Slutsatsen blev även att det skiljer sig avsevärt mycket i precision om man använder sig av för få eller helt enkelt dåligt placerade beacons.Different navigation techniques are something that people uses on a daily basis. It has been and still is an important part in the history of mankind, initially navigation was about being able to navigate at sea using the stars, and today many people around the world have some kind of smartphone that can be used as a navigation tool. Indoor positioning is something that has become a hot topic in the last decade and since GPS has bad precision indoors new technologies has been developed to navigate and position objects indoors. Some leading technologies now includes Bluetooth Low Energy beacons and Wi-Fi, where you can position a user indoors using triangulation-techniques. The question still remains, which technique is viable for indoor positioning. This study has been conducted as a combination of two strategies, starting with a comparison study of three different positioning techniques, BLE, Wi-Fi and GPS, to come to a conclusion which positioning technique is suitable for indoor positioning. After the conclusion we evaluated the technique in the form of an experiment in which several factors, mainly the precision, are tested. In this study, BLE beacon were used as the positioning tool because it was easy to implement but also to able to achieve high precision in the right environment. To test the beacons, a light application prototype was developed that had the main purpose of positioning a user in a coordinate system using XY values. By using triangulation to position the user. The study achieved a mean margin of approximately 76 centimeters when five beacons were used in a 4 x 3 meter area. The conclusion was BLE beacons is a suitable tool when it comes to indoor positioning and that there is a lot of difference in precision when using too few or poorly placed beacons.
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Liu, Honggang. "Research and implementation of an indoor positioning algorithm." Thesis, Mittuniversitetet, Avdelningen för informationssystem och -teknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-32394.
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The goal of the Internet of Things’ sensing technology is to provide LBS(location-based services); a key technology is finding out how to positioning the sensing devices. For positioning outdoors, mature tech-nology such as GPS and cellular network location can be used. There is little research about indoor positioning, and there is no finished product on the market. This paper shows how to use both Wi-Fi and ZigBee signal for position-ing; Wi-Fi to find the area position and ZigBee to find the coordinate position. The main contribution of this paper is described in the follow-ing: This paper will present an algorithm using kNN on a Wi-Fi signal, as a way to find the location area of users. The GPS signal cannot be used indoors, but there are usually numerous Wi-Fi signals, that can be used for indoor positioning. In this design, to build a dataset containing the number of locations and the Wi-Fi signal strength list of each location. When indoor positioning is needed, the KNN algorithm is used to compare the user’s Wi-Fi signal strength with the dataset and find the location number. When precise positioning is needed, the ZigBee signal should be used. In this paper two different methods for precise positioning in are used, one is an improved algorithm of triangle centroid algorithm where the positioning accuracy depends on the number of anchor points and the interval of each point. The other method is the neural network method. This method could give stable result with only four anchor points. Finally, there is a comparison of the methods mentioned in this paper : the Wi-Fi fingerprint method, the ZigBee triangle centroid algorithm, and neural network method.
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Öhrström, Tobias, and Christoffer Olsson. "The precision of RSSI-fingerprinting based on connected Wi-Fi devices." Thesis, Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-12161.
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Received Signal Strength Indication (RSSI) fingerprinting is a popular technique in the fieldof indoor positioning. Many studies on the subject exist acknowledging Wi-Fi signal variationconnected to Wi-Fi signals, but does not discuss possible signal variation created byconnected devices nor consequential precision loss.Understanding more about the origins of signal variation in received signal strength indication(RSSI) fingerprinting would help deal with or prevent them as well as provide moreknowledge for applications based on such signals. Environments with a varying number ofconnected devices would benefit from knowing changes in localization precision resultingfrom the devices connecting and disconnecting from the access point because it wouldindicate whether workarounds for such circumstances would be necessary.To address this issue, the work presented here focuses on how the precision of RSSIfingerprinting vary given different levels of connected Wi-Fi devices. It was carried out byconducting real world experiments at times of low- and normal levels of connected devices toaccess points on two separate locations and evaluating precision changes between statedactivity levels. These experiments took place at the University of Borås as well as at Ericssonin Borås.Experimental findings indicate that the accuracy does deteriorate in higher levels of activitythan in low activity, even though not enough evidence to determine the precision ofdeterioration. The experiments thereby provide a foundation for location-based applicationsand services that can communicate the level of positional error that exist in differentenvironments which would make the users aware but also make the applications adaptaccordingly to different environments. Based on the precision achieved, we identify variousapplications that would benefit from our proposed model. These were applications that wouldtrack mobile resources, find immobile resources, find the movement flows of users as well asnavigation- and Wi-Fi coverage applications.Further research for investigating the exact correlation between access point stress andprecision loss is proposed to fully understand the implications connected devices have onRSSI fingerprinting.
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Davodi, Rashed, and Jonatan Högberg. "Radiobaserad spårning av rörlig utrustning inomhus." Thesis, Högskolan i Gävle, Datavetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-24714.
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Idre, Andreas, and Elin Lindesten. "En förenklad beslutsmodell av Real Time Locating System inom verkstadsindustrin : En fallstudie av tre produktionsenheter på företaget Scania." Thesis, KTH, Tillämpad maskinteknik (KTH Södertälje), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-232194.
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Under de senaste åren har intresset för att positionera objekt inomhus ökat tack vare den tilltagande trenden kring Internet of Things (IOT). I den tekniska världen återfinns ett flertal begrepp för inomhuspositionering, däribland Real Time Locating System (RTLS). RTLS främsta egenskap är att identifiera och lokalisera objekt inomhus, kvalitetssäkra processer, informationsutbyte mellan aktörer, alarmera om objekt rör sig utanför vissa gränser. Gemensamt är att det ska effektivisera processer i flödet. En verkstadsindustri, som Scania CV AB, karaktäriseras av maskiner, rörelse, buller, personal och material. Materialet som återfinns på Scania kan påverka RTLSteknologin. Det finns idag inte en dominerande teknik för inomhuspositionering därav att det har genererat i flertalet tekniker däribland UWB, RFID, IR, BLE, Wi-Fi. Rapportens frågeställningar är vilka utvärderingskriterier som styr valet av RTLSsystem och vilka av de här kriterierna är mer kritiska än andra? samt vilka tekniker kan användas i en verkstadsindustrin idag? För att besvara rapportens frågeställningar har ett kvalitativt förhållningssätt använts. Rapporten har använt fallstudie för att kunna svara på studiens frågeställningar. En kvalitativ ansats har använts med intervjuer och observationer som genomförande på produktionsenheterna. Tidigare forskning har legat till grund för utformningen av utvärderingskriterierna. Utvärderingskriterierna har i rapportens analysdel vägts in mot teknikens förutsättningar, omgivningens preferenser och mot det specifika behovet. Slutsatsen är att behovet och omgivningen är de två viktigaste elementen att beakta vid ett beslutsfattande av RTLS-system. I ett beslutsfattande är vissa kriterier mer kritiska än andra. Då behovet styr valet av teknik blir således samtliga tekniker användbara i en verkstadsindustri. Det finns dock ingen teknik som passar samtliga behov varför det således blir viktigt att välja en leverantör med bred produktportfölj som har möjlighet att erbjuda hybrider.During the recent years the interest of positioning has increased due to the growing trend of Internet of Things (IoT). In the technical world there are several concepts for indoor positioning, among them Real Time Locating System (RTLS). RTLS main feature is to identify and locate objects indoors, to insure the quality of processes, to exchange information between stakeholder and to alert if items move beyond certain limits. Common is that it will streamline working processes. A engineering industry, such as Scania CV AB, is characterized by objects such as machines, motions, noise, personnel and materials. These objects found in Scania may affect the RTLS technology. There is currently no dominant indoor positioning technology on the market, thus there are many different technologies including UWB, RFID, IR, BLE and Wi-Fi. The problem definition of the thesis is therefore “What evaluation criteria are affecting the decision-making process when choosing RTLS and what criteria is more critical than the other?” and “Which technologies can be used in a engineering industry today?” To answer these problem definitions a qualitative research has been issued structured by a case study. Interviews and observations has been carried out as methods at the production units. The evaluation criteria are based on previous research. The criteria have been weighed against the technical conditions, the preferences of the environment and the specific use case. The conclusion is that the use case and the environment are the two most important elements to consider when deciding on a RTLS technology. In decision-making some criteria are more critical than others. As use case directs what technology should be used, all technologies are considered to be useful in a engineering industry. However there is no technology that fits all applications, thus it is important to choose a vendor that has a comprehensive product portfolio and one that enables hybrid solutions.
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THUMMALAPALLI, STALINBABU. "Wi-Fi Indoor Positioning." Thesis, Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-19901.
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The Global Positioning System (GPS) is a space based satellite navigation system. It provides location and time information in all weather, anywhere on the earth. Unfortunately GPS fails to give position indoors, because it requires a direct line of sight to several satellites. Indoor locating systems can thus not use GPS, because signal strengths are weakened or cancelled by building structures. So we need another technology for positioning indoors. Wireless indoor positioning systems are very popular in recent years. These systems are successfully used to asset tracking. By using ultrasound or lasers we can find accurate positioning, but this involves larger costs and energy requirements.Indoor wireless positioning based on received RF signal strength has gained more popularity for researchers in recent years. Wireless communication is a rapidly growing technology used in both home and business networking. Currently wireless networks are set up in institutes, hospitals, shopping malls, and airports and so on. Wi-Fi location determination is a technology; it utilizes existing Wi-Fi equipment such as those installed in personal computers, PDAs and mobile phones. The technology uses modulated Wi-Fi transmission signals to detect the presence of a device, which does not necessarily have to be connected to the network. The system is able to triangulate the position of the device based on the signals received from several access points. Some researchers implemented positioning algorithms to find the position indoors. In those algorithms some popular algorithms are signal strength mean value algorithm, K nearest neighbor’s algorithm, and Bayesian positioning algorithm. Before positioning, we can also measure the signal strength values in a reference point inside the building and use those values to build a database. The database contains coordinates of reference points, orientation and set of signal strength measurements linked to the access points. In positioning phase we can then measure the signal strength and compare those signals with an already built database for finding the position. This type of position finding is known as finger printing method.This paper provides an overview of the existing positioning techniques. The main aim of this thesis is to find the accurate position indoors. For finding the accurate position we are using the finger print database model. In addition to the finger print database model we are considering the walking speed of the user and the history of previous signal strength values. In this thesis we proposed a User Prediction Algorithm, using this algorithm we can find the position of object or user with less error and also we can solve the ambiguity problem to some extent.
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Ta, Viet-Cuong. "Smartphone-based indoor positioning using Wi-Fi, inertial sensors and Bluetooth." Thesis, Université Grenoble Alpes (ComUE), 2017. http://www.theses.fr/2017GREAM092/document.
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Grâce à l’émergence dans la vie quotidienne des appareils de plus en plus populaires que sont les smartphones et les tablettes, la tâche de postionner l'utilisateur par le biais de son téléphone est une problématique fortement étudiée dans les domaines non seulement de la recherche mais également des communautés industrielles. Parmi ces technologies, les approches GPS sont devenues une norme et ont beaucoup de succès pour une localisation en environnement extérieur. Par contre, le Wi-Fi, les capteurs inertiels et le Bluetooth sont plutôt préférés pour les tâches de positionnement dans un environnement intérieur.Pour ce qui concerne le positionnement des smartphones, les approches basées sur les « empreintes digitales » (fingerprint) Wi-Fi sont bien établies. D'une manière générale, ces approches tentent d'apprendre la fonction de correspondance (cartographie) des caractéristiques du signal Wi-Fi par rapport à la position de l’appareil dans le monde réel. Elles nécessitent généralement une grande quantité de données pour obtenir une bonne cartographie. Lorsque ces données d'entraînement disponibles sont limitées, l'approche basée sur les empreintes digitales montre alors des taux d’erreurs élevés et devient moins stable. Dans nos travaux, nous explorons d’autres approches, différentes, pour faire face à cette problématique du manque de données d'entraînement. Toutes ces méthodes sont testées sur un ensemble de données public qui est utilisé lors d’une compétition internationale à la Conférence IPIN 2016.En plus du système de positionnement basé sur la technologie Wi-Fi, les capteurs inertiels du smartphone sont également utiles pour la tâche de suivi. Les trois types de capteurs, qui sont les accéléromètres, le gyroscope et la boussole magnétique, peuvent être utilisés pour suivre l'étape et la direction de l'utilisateur (méthode SHS). Le nombre d'étapes et la distance de déplacement de l'utilisateur sont calculés en utilisant les données de l'accéléromètre. La position de l'utilisateur est calculée par trois types de données avec trois méthodes comprenant la matrice de rotation, le filtre complémentaire et le filtre de Madgwick. Il est raisonnable de combiner les sorties SHS avec les sorties de Wi-Fi, car les deux technologies sont présentes dans les smartphones et se complètent. Deux approches combinées sont testées. La première approche consiste à utiliser directement les sorties Wi-Fi comme points de pivot pour la fixation de la partie de suivi SHS. Dans la deuxième approche, nous comptons sur le signal Wi-Fi pour construire un modèle d'observation, qui est ensuite intégré à l'étape d'approximation du filtre à particules. Ces combinaisons montrent une amélioration significative par rapport au suivi SHS ou au suivi Wi-Fi uniquement.Dans un contexte multiutilisateur, la technologie Bluetooth du smartphone pourrait fournir une distance approximative entre les utilisateurs. La distance relative est calculée à partir du processus de numérisation du périphérique Bluetooth. Elle est ensuite utilisée pour améliorer la sortie des modèles de positionnement Wi-Fi. Nous étudions deux méthodes. La première vise à créer une fonction d'erreur qui permet de modéliser le bruit dans la sortie Wi-Fi et la distance approximative produite par le Bluetooth pour chaque intervalle de temps spécifié. La seconde méthode considère par contre cette relation temporelle et la contrainte de mouvement lorsque l'utilisateur se déplace. Le modèle d'observation du filtre à particules est une combinaison entre les données Wi-Fi et les données Bluetooth. Les deux approches sont testées en fonction de données réelles, qui incluent jusqu'à quatre utilisateurs différents qui se déplacent dans un bureau. Alors que la première approche n'est applicable que dans certains scénarios spécifiques, la deuxième approche montre une amélioration significative par rapport aux résultats de position basés uniquement sur le modèle d'empreintes digitales Wi-FiWith the popularity of smartphones and tablets in daily life, the task of finding user’s position through their phone gains much attention from both the research and industry communities. Technologies integrated in smartphones such as GPS, Wi-Fi, Bluetooth and camera are all capable for building a positioning system. Among those technologies, GPS has approaches have become a standard and achieved much success for the outdoor environment. Meanwhile, Wi-Fi, inertial sensors and Bluetooth are more preferred for positioning task in indoor environment.For smartphone positioning, Wi-Fi fingerprinting based approaches are well established within the field. Generally speaking, the approaches attempt to learn the mapping function from Wi-Fi signal characteristics to the real world position. They usually require a good amount of data for finding a good mapping. When the available training data is limited, the fingerprinting-based approach has high errors and becomes less stable. In our works, we want to explore different approaches of Wi-Fi fingerprinting methods for dealing with a lacking in training data. Based on the performance of the individual approaches, several ensemble strategies are proposed to improve the overall positioning performance. All the proposed methods are tested against a published dataset, which is used as the competition data of the IPIN 2016 Conference with offsite track (track 3).Besides the positioning system based on Wi-Fi technology, the smartphone’s inertial sensors are also useful for the tracking task. The three types of sensors, which are accelerate, gyroscope and magnetic, can be employed to create a Step-And-Heading (SHS) system. Several methods are tested in our approaches. The number of steps and user’s moving distance are calculated from the accelerometer data. The user’s heading is calculated from the three types of data with three methods, including rotation matrix, Complimentary Filter and Madgwick Filter. It is reasonable to combine SHS outputs with the outputs from Wi-Fi due to both technologies are present in the smartphone. Two combination approaches are tested. The first approach is to use directly the Wi-Fi outputs as pivot points for fixing the SHS tracking part. In the second approach, we rely on the Wi-Fi signal to build an observation model, which is then integrated into the particle filter approximation step. The combining paths have a significant improvement from the SHS tracking only and the Wi-Fi only. Although, SHS tracking with Wi-Fi fingerprinting improvement achieves promising results, it has a number of limitations such as requiring additional sensors calibration efforts and restriction on smartphone handling positions.In the context of multiple users, Bluetooth technology on the smartphone could provide the approximated distance between users. The relative distance is calculated from the Bluetooth inquiry process. It is then used to improve the output from Wi-Fi positioning models. We study two different combination methods. The first method aims to build an error function which is possible to model the noise in the Wi-Fi output and Bluetooth approximated distance for each specific time interval. It ignores the temporal relationship between successive Wi-Fi outputs. Position adjustments are then computed by minimizing the error function. The second method considers the temporal relationship and the movement constraint when the user moves around the area. The tracking step are carried out by using particle filter. The observation model of the particle filter are a combination between the Wi-Fi data and Bluetooth data. Both approaches are tested against real data, which include up to four different users moving in an office environment. While the first approach is only applicable in some specific scenarios, the second approach has a significant improvement from the position output based on Wi-Fi fingerprinting model only
Book chapters on the topic "Wi-Fi Indoor positioning system"
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Sivers, Mstislav, Grigoriy Fokin, Pavel Dmitriev, Artem Kireev, Dmitry Volgushev, and Al-odhari Abdulwahab Hussein Ali. "Wi-Fi Based Indoor Positioning System Using Inertial Measurements." In Lecture Notes in Computer Science, 734–44. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67380-6_69.
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Hong, Jaemin, KyuJin Kim, and ChongGun Kim. "Comparison of Indoor Positioning System Using Wi-Fi and UWB." In Intelligent Information and Database Systems, 623–32. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75417-8_58.
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Cypriani, Matteo, Philippe Canalda, and François Spies. "OwlPS: A Self-calibrated Fingerprint-Based Wi-Fi Positioning System." In Evaluating AAL Systems Through Competitive Benchmarking. Indoor Localization and Tracking, 36–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33533-4_4.
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Radaelli, Laura, Yael Moses, and Christian S. Jensen. "Using Cameras to Improve Wi-Fi Based Indoor Positioning." In Web and Wireless Geographical Information Systems, 166–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-55334-9_11.
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Yoo, Sang Guun, and Jhonattan J. Barriga. "Privacy-Aware Authentication for Wi-Fi Based Indoor Positioning Systems." In Applications and Techniques in Information Security, 201–13. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5421-1_17.
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Yu, Chongsheng, Xin Li, Lei Dou, Jianwei Li, Yu Zhang, Jian Qin, Yuqing Sun, and Zhiyue Cao. "Implement and Optimization of Indoor Positioning System Based on Wi-Fi Signal." In Algorithms and Architectures for Parallel Processing, 220–28. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49583-5_17.
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Geng, Yuyang, Shuhang Zhang, Hangbin Wu, and Chaoyang Hu. "Improved Indoor Positioning System Based on Wi-Fi RSSI: Design and Deployment." In Lecture Notes in Geoinformation and Cartography, 31–45. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04028-8_3.
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Sa’ahiry, A. H. A., A. H. Ismail, L. M. Kamarudin, A. Zakaria, and H. Nishizaki. "An Experimental Study of Deep Learning Approach for Indoor Positioning System Using WI-FI System." In Lecture Notes in Mechanical Engineering, 113–24. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0866-7_9.
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Akram, Beenish Ayesha, Ali Hammad Akbar, Bilal Wajid, Omair Shafiq, and Amna Zafar. "LocSwayamwar: Finding a Suitable ML Algorithm for Wi-Fi Fingerprinting Based Indoor Positioning System." In Lecture Notes in Electrical Engineering, 111–23. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0408-8_10.
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Barriga A., Jhonattan J., Sang Guun Yoo, and Juan Carlos Polo. "Enhancement to the Privacy-Aware Authentication for Wi-Fi Based Indoor Positioning Systems." In Lecture Notes in Computer Science, 143–55. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29729-9_8.
Full textConference papers on the topic "Wi-Fi Indoor positioning system"
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Costilla-Reyes, Omar, and Kamesh Namuduri. "Dynamic Wi-Fi fingerprinting indoor positioning system." In 2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN). IEEE, 2014. http://dx.doi.org/10.1109/ipin.2014.7275493.
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Vaupel, Thorsten, Jochen Seitz, Frederic Kiefer, Stephan Haimerl, and Jorn Thielecke. "Wi-Fi positioning: System considerations and device calibration." In 2010 International Conference on Indoor Positioning and Indoor Navigation (IPIN). IEEE, 2010. http://dx.doi.org/10.1109/ipin.2010.5646207.
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Jacq, David, Pascal Chatonnay, Christelle Bloch, Philippe Canalda, and Francois Spies. "Towards zero-configuration for Wi-Fi indoor positioning system." In 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN). IEEE, 2017. http://dx.doi.org/10.1109/ipin.2017.8115951.
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Cypriani, Matteo, Philippe Canalda, François Spies, and Ancuta Dobircau. "Benchmark measurements for Wi-Fi signal strength-based positioning system." In 2012 International Conference on Indoor Positioning and Indoor Navigation (IPIN). IEEE, 2012. http://dx.doi.org/10.1109/ipin.2012.6418854.
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Cypriani, Matteo, Frederic Lassabe, Philippe Canalda, and Francois Spies. "Open Wireless Positioning System: A Wi-Fi-Based Indoor Positioning System." In 2009 IEEE Vehicular Technology Conference (VTC 2009-Fall). IEEE, 2009. http://dx.doi.org/10.1109/vetecf.2009.5378966.
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Doiphode, Siddhesh R., J. W. Bakal, and Madhuri Gedam. "A hybrid indoor positioning system based on Wi-Fi hotspot and Wi-Fi fixed nodes." In 2016 IEEE International Conference on Engineering and Technology (ICETECH). IEEE, 2016. http://dx.doi.org/10.1109/icetech.2016.7569191.
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Ahn, Jeonghee, and Dongsoo Han. "Crowd-assisted radio map construction for Wi-Fi positioning systems." In 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN). IEEE, 2017. http://dx.doi.org/10.1109/ipin.2017.8115872.
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Brian Bai, Yuntian, Tao Gu, and Andong Hu. "Integrating Wi-Fi and magnetic field for fingerprinting based indoor positioning system." In 2016 7th International Conference on Indoor Positioning and Indoor Navigation (IPIN). IEEE, 2016. http://dx.doi.org/10.1109/ipin.2016.7743699.
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Narzullaev, Anvar, and Hasan Selamat Mohd. "Wi-Fi signal strengths database construction for indoor positioning systems using Wi-Fi RFID." In 2013 IEEE International Conference on RFID-Technologies and Applications (RFID-TA 2013). IEEE, 2013. http://dx.doi.org/10.1109/rfid-ta.2013.6694518.
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Wang, Fan, Zhengyong Huang, Hui Yu, Xiaohua Tian, Xinbing Wang, and Jinwei Huang. "EESM-based fingerprint algorithm for Wi-Fi indoor positioning system." In 2013 IEEE/CIC International Conference on Communications in China (ICCC). IEEE, 2013. http://dx.doi.org/10.1109/iccchina.2013.6671197.
Full textReports on the topic "Wi-Fi Indoor positioning system"
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Tran, Huy. Context-Aware Wi-Fi Infrastructure-based Indoor Positioning Systems. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6885.
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