This study demonstrates a WiFi indoor positioning system using Deep Learning algorithms. A new method using fitting function in MATLAB will be utilized to compute the path loss coefficient and log-normal fading variance. To reduce the error, a new hybrid localization approach utilizing Received Signal Strength Indicator (RSSI) and Angle of Arrival (AoA) has been created. Three Deep Learning algorithms would be utilized to decrease the adverse influence of the noise and interference. This paper compares the performance of two models in three different indoor environments. The average error of our hybrid positioning model trained by CNN in the big classroom is less than 250 mm.
K-Neares Neighbors (KNN) and its variant weighted KNN (WKNN) have been explored for years in both academy and industry to provide stable and reliable performance in WiFi-based indoor positioning systems. Such algorithms estimate the location of a given point based on the locality information from the selected nearest WiFi neighbors according to some distance metrics calculated from the combination of WiFi received signal strength (RSS). However, such a process does not consider the relational information among the given point, WiFi neighbors, and the WiFi access points (WAPs). Therefore, this …
Currently there is no standard indoor positioning system, similar to outdoor GPS. However, WiFi signals have been used in a large number of proposals to achieve the above positioning, many of which use machine learning to do so. But what are the most commonly used techniques in machine learning? What accuracy do they achieve? Where have they been tested? This article presents a systematic review of works between 2019 and 2021 that use WiFi as the signal for positioning and machine learning models to estimate indoor position. 64 papers have been identified as relevant, which have been systemati…
WiFi-based indoor localization has received extensive attentions from both academia and industry. However, the overhead of constructing and maintaining the WiFi fingerprint map remains a bottleneck for the wide-deployment of WiFi-based indoor localization systems. Recently, robots are adopted as the professional surveyor to fingerprint the environment autonomously. But the time and energy cost still limit the coverage of the robot surveyor, thus reduce its scalability. To fill this need, we design an AutonomousWiFi Fingerprinting system, called AuF, which autonomously constructs the fingerprin…
The accuracy of smartphone-based positioning methods using WiFi usually suffers from ranging errors caused by non-line-of-sight (NLOS) conditions. Previous research usually exploits several statistical features from a long time series (hundreds of samples) of WiFi received signal strength (RSS) or WiFi round-trip time (RTT) to achieve a high identification accuracy. However, the long time series or large sample size attributes to high power and time consumption in data collection for both training and testing. This will also undoubtedly be detrimental to user experience as the waiting time of …
The past years have witnessed increasing research interest in achieving passive human localization with commodity WiFi devices. However, due to the fundamental limited spatial resolution of WiFi signals, it is still very difficult to achieve accurate localization with existing commodity WiFi devices. To tackle this problem, in this paper, we propose to exploit the degree of freedom provided by the Intelligent Reflecting Surface (IRS), which is composed of a large number of controllable reflective elements, to modulate the spatial distribution of WiFi signals and thus break down the spatial res…
Precise indoor localization is an increasingly demanding requirement for various emerging applications, like Virtual/Augmented reality and personalized advertising. Current indoor environments are equipped with pluralities of WiFi access points (APs), whose deployment is expected to be massive in the future enabling highly precise localization approaches. Though the conventional model-based localization schemes have achieved sub-meter level accuracy by fusing multiple channel state information (CSI) observations, the corresponding computational overhead is usually significant, especially in th…
Huazhong University of Science and Technology（华中科技大学）
This paper presents an indoor pose estimation system for micro aerial vehicles (MAVs) with a single WiFi access point. Conventional approaches based on computer vision are limited by illumination conditions and environmental texture. Our system is free of visual limitations and instantly deployable, working upon existing WiFi infrastructure without any deployment cost. Our system consists of two coupled modules. First, we propose an angle-of-arrival (AoA) estimation algorithm to estimate MAV attitudes and disentangle the AoA for positioning. Second, we formulate a WiFi-inertial sensor fusion m…
University of Kassel（卡塞尔大学）；Interdisciplinary Research Center for Information System Design (ITeG)（信息系统设计跨学科研究中心（ITeG））
The ubiquitous presence of WiFi access points and mobile devices capable of measuring WiFi signal strengths allow for real-world applications in indoor localization and mapping. In particular, no additional infrastructure is required. Previous approaches in this field were, however, often hindered by problems such as effortful map-building processes, changing environments and hardware differences. We tackle these problems focussing on topological maps. These represent discrete locations, such as rooms, and their relations, e.g., distances and transition frequencies. In our unsupervised method,…
This study describes a UWB and Machine Learning (ML)-based indoor positioning system. We propose a simple mathematical strategy to create data to reduce the job of measurements for fingerprint-based indoor localization systems. A considerable number of measurements can be avoided this way. The paper compares and contrasts the performance of four distinct models. Most test locations' average error may be reduced to less than 150 mm using the best model.
Due to the indoor none-line-of-sight (NLoS) propagation and multi-access interference (MAI), it is a great challenge to achieve centimeter-level positioning accuracy in indoor scenarios. However, the sixth generation (6G) wireless communications provide a good opportunity for the centimeter-level positioning. In 6G, the millimeter wave (mmWave) and terahertz (THz) communications have ultra-broad bandwidth so that the channel state information (CSI) will have a high resolution. In this paper, a weighted random forest (WRF) based indoor positioning algorithm using CSI based channel fingerprint f…
The fact that almost every person owns a smartphone device that can be precisely located is both empowering and worrying. If methods for accurate tracking of devices (and their owners) via WiFi probing are developed in a responsible way, they could be applied in many different fields, from data security to urban planning. Numerous approaches to data collection and analysis have been covered, some of which use active sensing equipment, while others rely on passive probing, which takes advantage of nearly universal smartphone usage and WiFi network coverage. In this study, we introduce a system …
We examined UWB-based indoor location in conjunction with a fingerprint technique in this work. We built a connection between the measured and real distances for the UWB indoor positioning system. This connection is used to produce a distance database that may be used to generate fringerprints. We created a BP neural network to classify the target node to the relevant fringerpint using the distance database. Our suggested deep learning technology considerably enhances location accuracy when compared to existing trilateration systems.
Indoor localization becomes a raising demand in our daily lives. Due to the massive deployment in the indoor environment nowadays, WiFi systems have been applied to high accurate localization recently. Although the traditional model based localization scheme can achieve sub-meter level accuracy by fusing multiple channel state information (CSI) observations, the corresponding computational overhead is significant. To address this issue, the model-free localization approach using deep learning framework has been proposed and the classification based technique is applied. In this paper, instead …
In this communication revolution era, visible light communication (VLC) is the optimum efficacious answer to the increased request for high-speed data transmission with reduced cost, besides the illumination. This technology is considered the best promising candidate for indoor positioning due to its ability to attain higher positioning accuracy than other technologies, alongside its distinct advantages. However, the existing techniques that had been applied in an indoor positioning system (IPS) based on VLC focused on the aspect of accuracy but neglected the aspects of cost and complexity. Th…
Indoor positioning is one of the core technologies of Internet of Things (IoT) and artificial intelligence (AI), and is expected to play a significant role in the upcoming era of AI. However, affected by the complexity of indoor environments, it is still highly challenging to achieve continuous and reliable indoor positioning. Currently, 5G cellular networks are being deployed worldwide, the new technologies of which have brought the approaches for improving the performance of wireless indoor positioning. In this paper, we investigate the indoor positioning under the 5G new radio (NR), which h…
In recent years, with the increasing demand for indoor positioning service, visible light indoor positioning based on image sensors has been widely studied. However, many researches only put forward the relevant localization algorithm and did not make a deep discussion on the principle of the visible light localization. In this paper, we make a deep discussion on the principle of the two-light positioning algorithm and the three-light positioning algorithm based on the image sensor, which includes how these positioning algorithms work and the errors analysis. Based on the discussion above, we …
The location fingerprinting method, which typically utilizes supervised learning, has been widely adopted as a viable solution for the indoor positioning problem. Many indoor positioning datasets are imbalanced. Models trained on imbalanced datasets may exhibit poor performance on the minority class(es). This problem, also known as the "curse of imbalanced data," becomes more evident when class distributions are highly imbalanced. Motivated by the recent advances in deep generative modeling, this paper proposes using Variational Autoencoders and Conditional Variational Autoencoders as oversamp…
University of Electronic Science and Technology of China（电子科技大学）
Based on various existing wireless fingerprint location algorithms in intelligent sports venues, a high-precision and fast indoor location algorithm improved weighted k-nearest neighbor (I-WKNN) is proposed. In order to meet the complex environment of sports venues and the demand of high-speed sampling, this paper proposes an AP selection algorithm for offline and online stages. Based on the characteristics of the signal intensity distribution in intelligent venues, an asymmetric Gaussian filter algorithm is proposed. This paper introduces the application of the positioning algorithm in the in…
In daily life, mobile and wearable devices with high computing power, together with anchors deployed in indoor environments, form a common solution for the increasing demands for indoor location-based services. Within the technologies and methods currently in use for indoor localization, the approaches that rely on Bluetooth Low Energy (BLE) anchors, Received Signal Strength (RSS), and lateration are among the most popular, mainly because of their cheap and easy deployment and accessible infrastructure by a variety of devices. Nevertheless, such BLE- and RSS-based indoor positioning systems ar…
Recently, round-trip time (RTT) measured by a fine-timing measurement protocol has received great attention in the area of WiFi positioning. It provides an acceptable ranging accuracy in favorable environments when a line-of-sight (LOS) path exists. Otherwise, a signal is detoured along with non-LOS paths, making the resultant ranging results different from the ground-truth, called an RTT bias, which is the main reason for poor positioning performance. To address it, we aim at leveraging the user mobility trajectory detected by a smartphone's inertial measurement units, called pedestrian dead …
The localization speed and accuracy in the indoor scenario can greatly impact the Quality of Experience of the user. While many individual machine learning models can achieve comparable positioning performance, their prediction mechanisms offer different complexity to the system. In this work, we propose a fingerprinting positioning method for multi-building and multi-floor deployments, composed of a cascade of three models for building classification, floor classification, and 2D localization regression. We conduct an exhaustive search for the optimally performing one in each step of the casc…
With a significant increase in area throughput, Massive MIMO has become an enabling technology for fifth generation (5G) wireless mobile communication systems. Although prototypes were built, an openly available dataset for channel impulse responses to verify assumptions, e.g. regarding channel sparsity, is not yet available. In this paper, we introduce a novel channel sounder architecture, capable of measuring multiantenna and multi-subcarrier channel state information (CSI) at different frequency bands, antenna geometries and propagation environments. The channel sounder has been verified by…
Visible light communication (VLC) utilizes light-emitting diodes (LEDs) to transmit wireless data. A VLC network can also be used to localize mobile users in indoor environments, where the global positioning system (GPS) signals are weak. However, the line-of-sight (LOS) links of mobile VLC devices can be blocked easily, which decreases the accuracy of localization. In this paper, we study tracking a VLC user when the availability of VLC access point (AP) link changes over the user's route. We propose a localization method for a single available AP and use known estimation methods when a large…
In this paper, an indoor robot localization system based on Robot Operating System (ROS) and visible light communication (VLC) is presented. On the basis of our previous work, we innovatively designed a VLC localization and navigation package based on Robot Operating System (ROS), which contains the LED-ID detection and recognition method, the video target tracking algorithm and the double-lamp positioning algorithm. This package exploited the principle of double-lamp positioning and the loose coupling characteristics of the ROS system, which is implemented by loosely coupled ROS nodes. Conseq…
Outdoor positioning systems based on the Global Navigation Satellite System have several shortcomings that have deemed their use for indoor positioning impractical. Location fingerprinting, which utilizes machine learning, has emerged as a viable method and solution for indoor positioning due to its simple concept and accurate performance. In the past, shallow learning algorithms were traditionally used in location fingerprinting. Recently, the research community started utilizing deep learning methods for fingerprinting after witnessing the great success and superiority these methods have ove…
The proposed infrastructure, named Techtile, provides a unique R&D facility as features dispersed electronics enables transmission and capturing of a multitude of signals in 3D. Specific available equipment that enhances the design process from smooth prototyping to a commercial product is discussed. The acoustic parameters of the room, particularly the reverberation and ambient noise, are measured to take these into account for future innovative acoustic indoor positioning and sensing systems. This can have a positive influence on the accuracy and precision. The wooden construction represents…
University of Melbourne（墨尔本大学）；China University of Geosciences（中国地质大学）；National Engineering Research Center for Geographic Information System（国家地理信息系统工程技术研究中心）
Fingerprinting is a popular indoor localization technique since it can utilize existing infrastructures (e.g., access points). However, its site survey process is a labor-intensive and time-consuming task, which limits the application of such systems in practice. In this paper, motivated by the availability of advanced sensing capabilities in smartphones, we propose a fast and reliable fingerprint collection method to reduce the time and labor required for site survey. The proposed method uses a landmark graph-based method to automatically associate the collected fingerprints, which does not r…
Indoor positioning is currently recognized as one of the important features in emergency, commercial and industrial applications. The 5G network enhances mobility, flexibility, reliability, and security to new higher levels which greatly benefit the IoT and industrial applications. Industrial IoT (IIoT) use-cases are characterized by ambitious system requirements for positioning accuracy in many verticals. For example, on the factory floor, it is important to locate assets and moving objects such as forklifts. The deployment design for different IIoT environments has a significant impact on th…
Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, China（中国教育部航空航天信息安全与可信计算重点实验室）；School of Cyber Science and Engineering, Wuhan University, China（中国武汉大学网络空间安全学院）
Multi-source fusion positioning is one of the technical frameworks for obtaining sufficient indoor positioning accuracy. In order to evaluate the effect of multi-source fusion positioning, it is necessary to establish a fusion error model. In this paper, we first use the least squares method to fuse the radio fingerprint and the PDR positioning, and then apply the variance propagation laws to calculate the error distribution of indoor multi-source localization methods. Based on the fusion error model, we developed an indoor positioning simulation system. The system can give a better positionin…
WiFi-based indoor localization has now matured for over a decade. Most of the current localization algorithms rely on the WiFi access points (APs) in the enterprise network to localize the WiFi user accurately. Thus, the WiFi user's location information could be easily snooped by an attacker listening through a compromised WiFi AP. With indoor localization and navigation being the next step towards automation, it is important to give users the capability to defend against such attacks. In this paper, we present MIRAGE, a system that can utilize the downlink physical layer information to create…
University of Brighton（布莱顿大学）；Royal Holloway, University of London（伦敦大学皇家霍洛威学院）；Zhejiang University（浙江大学）
The continual proliferation of mobile devices has encouraged much effort in using the smartphones for indoor positioning. This article is dedicated to review the most recent and interesting smartphones based indoor navigation systems, ranging from electromagnetic to inertia to visible light ones, with an emphasis on their unique challenges and potential real-world applications. A taxonomy of smartphones sensors will be introduced, which serves as the basis to categorise different positioning systems for reviewing. A set of criteria to be used for the evaluation purpose will be devised. For eac…
