A sector-based campus-wide indoor positioning system

In indoor positioning problems, GPS technology used in outdoor positioning needs to be improved due to the characteristic features of wireless signals. There currently needs to be a generally accepted standard method for indoor positioning. In this study, an ecosystem consisting of Beacon devices, Bluetooth intelligent devices, and Wi-Fi access points has been created to propose an effective indoor location determination method by using Wi-Fi and BLE technologies in a hybrid way. First, RSSI (Received Signal Strength Indicator) data were collected using the fingerprint method. Then, Kalman Filter and Savitzky Golay Filter are used in a hybrid manner to reduce the noise on the obtained signal data and make it more stable. In the first part, using the collected data from Wi-Fi and Beacon devices, the Non-linear least squares method (NLLS), including Levenberg-Marquardt (LM), is used for indoor tracking. In the second part, a fingerprinting-based approach is tested. K Nearest Neighbor (KNN) and Support Vector Machine (SVM) algorithms estimate the area where the client is located. Each algorithm’s accuracy rate are calculated on different training and test data and presented.

<p>The interest in indoor positioning systems has been rising in the last couple of decades. This interest was further accelerated by the availability of signals of opportunity. The introduction of signals of opportunity into the indoor environments has meant that these widely available technologies can be utilised to achieve higher levels of accuracy and precision for positioning in parking garages, airports, underground locations, large office buildings, and shopping centres. Indoor positioning can also be utilised for emergency services or for Building Information Models (BIM) for purposes such as construction sites or hospital building management. In addition to the need for indoor and outdoor positioning, there is a need to ensure seamless transitions in and out of these different environments. For example, building information from BIM could be utilised for navigation of BIM users that considers the architectural barriers (e.g., for wheelchairs, baby strollers, and transport of large equipment within the facility). For this, a positioning system capable of seamlessly transitioning in and out of buildings is needed. This paper proposes a multi-sensor and cooperative positioning method of seamless environment transition and presents the first architecture concept. For this purpose, Global Navigation Satellite System (GNSS) data are planned to be fused with Ultra Wide-Band (UWB), Inertial Measurement Unit (IMU), Wireless-Fidelity (Wi-Fi), visual camera and LiDAR (Light Detection and Ranging) data. All sensors required for such a positioning system, are already available on modern smartphones commonly used as low-cost platforms for pedestrian navigation and localisation. Newer smartphones are also equipped with LiDAR and UWB chips, which are a future signal of opportunity, just like Wi-Fi currently is. The transition to different environments will be determined based on the UWB anchors called identical anchors that will be used to transition from the global coordinate system for the outdoor environment to the local building environment for positioning in indoor environments. The proposed multi-sensor and cooperative positioning system is expected to be able to achieve decimetre-level accuracy.</p>

China will establish the Xihe System based on Beidou/GNSS, Mobile Communication, Internet and Satellite Communication system to realize the Seamless Outdoor and Indoor Positioning in Wide Area (SOIPWA) on personal mobile terminals. Indoor Positioning System (IPS) is one of the main components and effective supplement for indoor positioning signal resource to resolve the positioning signal coverage of blind areas in complicated indoor environment based on current 2G/3G communication network and existing equipment. According to the requirement, the IPS adopted the unified time-frequency reference which affects the accuracy of indoor positioning directly. This paper provides an acquisition method of high accuracy frequency source used in the indoor positioning signal supplement. Positioning signals from outdoor base stations are acquired by RF front end and tracked by the carrier loop and code loop adjusting. After that, positioning signals are locked and synchronized to extract the high accuracy frequency source used for IPS, which can be built with low cost and precisely integrated with an outdoor positioning system. In practical test, the method satisfies the need of the accuracy and stability in time-frequency reference for IPS with high frequency accuracy, quick start and low drift rate through adjusting high accuracy Voltage Controlled Oscillator (VCO) by Digital to Analog Converter (DAC) with the frequency difference result from the adaptive loop.

Location-based service is one of the most popular buzzwords in the field of U-city. Positioning a user is an essential ingredient of a location-based system on a U-city. For the outdoor positioning, GPS based practical solutions have been introduced. However, GPS measurement error is too big to be used for U-campus services because the size of a campus is relatively smaller than a city. We propose Relative-Interpolation Method in order to improve the correctness of outdoor positioning. Besides, indoor positioning is necessary for U-campus while GPS signal is not available inside buildings. For the indoor positioning, Cricket, Active Badge, and so on have been introduced. These methods require special equipments dedicated for positioning. Our method does not require such equipments because it determines the user’s position based on the receiver signal strength indicators (RSSI) from access points (AP) which are already installed for WLAN. The algorithm we are using for indoor positioning is a kind of finger prints method. However our algorithm builds a decision tree instead of a look-up table in the off-line phase. Therefore, our method is faster than existing indoor positioning methods in the real-time phase. We have integrated our indoor and outdoor positioning methods and implemented a prototype of our indoor-outdoor positioning method on a laptop. Our experimental results are discussed in this paper.

Since the well-established navigation systems such as GPS are ineffective in indoor environments, research into developing novel indoor positioning technologies has emerged in recent years. While several technologies are being investigated, a practical and reliable indoor positioning system is yet to emerge. Indoor positioning using light signals holds a great potential to provide a reliable solution to the indoor positioning problem. This paper presents a comprehensive review of the available literature on light-based indoor positioning systems. The paper establishes a classification based on the design of various light-based indoor positioning (LIP) systems, and discusses their key components including hardware, software and algorithms. The limitations and challenges of the LIP systems are discussed in details, and current published solutions are presented. The review also provides a comparison between LIP systems and other optical positioning systems, and identifies the associated research gaps. Finally, based on the identified gaps, the future research directions in both LIP systems and other optical positioning systems are presented.

The increasing demand of indoor Location Based Services (LBS) has made Indoor Positioning System (IPS) a hot research topic. Recently, various machine learning techniques have been developed to tackle the problems of IPS. The WLAN fingerprinting technique is one of the most cost effective choices for IPS due to the use of existing Wi-Fi networks in commercial and public buildings. However, the variation of Received Signal Strength (RSS) at any specific location due to the indoor fading issue leads to serious challenges in the accuracy of the distance estimates. In this paper, we propose a new technique by using Probabilistic Neural Network (PNN) multi-classifier based on Multiple Service Set Identifiers (MSSIDs) that are configured on the same access point. A spatial voting scenario for three PNN-classifiers is proposed as a tool to determine the location of the user. Using a spatial voting based technique is specifically designed to tackle the negative impact of the multi-path propagation on the performance of IPS. The proposed system is performed inside the College of Engineering and Applied Sciences (CEAS) at Western Michigan University. The performance of the proposed system compared with some of the common methods such as K-Nearest Neighbors (K-NN) and multi-class support vector machine (SVM). According to the experimental results, the spatial voting of the three PNN-dassifiers can significantly mitigate the adverse effects of RSS variation. The performance of the multi-classifier exhibits superior performance to that of the other single classifiers in all Area of Interest (AOI). Over 3000 m2 AOI, the proposed system achieves localization accuracy < 0.73m and with precision 90 % of the distance error less than 2m.

As a result of the drastic increase in the number of mobile device users, there is considerable public debate about possible health hazards especially due to base stations and Wi-Fi access points. For this reason, in this study the effects of the number of users in a base station and wireless fidelity (Wi-Fi) access point on electric field strength (E) levels were investigated using real-time measurements. Two-stage E measurements were performed on Ondokuz Mayıs University's (OMU) Kurupelit Campus with a PMM-8053 and SRM-3006 electromagnetic field (EMF) meter. In the first stage, 24-h measurements were performed with PMM-8053 at the location where the maximum E was measured and from which the busiest times of the day were then determined. The relationship among band selective E values was assessed using the number of users per minute provided by three cellular system operators for the location. Upper and lower bounds of E according to the number of users were calculated, and then an empirical model that helps calculate the E of medium with 86% accuracy was proposed. In the second stage, the effect of the number of users in a Wi-Fi access point on E level was investigated. For this purpose, the measurement environment was set as including one Wi-Fi access point with needed measurement devices. The number of users was increased as per user per minute. The number of users was validated with the data obtained from OMU IT Department. Using the measurement result, a model with a 96% accuracy between the E value in the environment and the number of users accessing the Wi-Fi system is proposed. With the use of these models, E level in the medium can be determined without using any EMF meters, thus precautions can be taken to stay within regulatory limits.

The aim of this research is to implement a precise Wi-Fi indoor positioning system (IPS) or localization system based upon the IEEE 802.11mc Fine-Timing Measurement (FTM) scheme also known as the Wi-Fi Round Trip Time (RTT) ranging technique, where ranging refers to a sub-process of positioning that determines the distance between a transmitter and receiver. Our system and its algorithms were implemented using a COTS (Commercial-Off-The-Shelf) smartphone and Wi-Fi access points. Experiments were conducted in several real-life indoor environments. This paper presents the detailed Wi-Fi RTT ranging performance of these devices in different system configurations and characterizes the systematic biases and noise model to improve the ranging accuracy. A novel three-step-positioning method is proposed to overcome the issues of no or multiple intersect points in trilateration due to ranging errors to improve positioning accuracy. This consists of first, systematic bias determination and removal; second, Clustering-based Trilateration (CbT) supported by Weighted Concentric Circle Generation (WCCG), namely CbT &amp; WCCG; third, positioning result and trajectory optimization using a Kalman filter. As a result, the evaluation experiments gave a position accuracy of ±1.2 m in 2D static positioning and ±1.3 m for dynamic motion tracking. Also, our CbT &amp; WCCG method demonstrate good tolerance against ranging errors. Moreover, the computational cost and positioning accuracy of CbT &amp; WCCG methods are compared with Least Square (LS) and Recursive Least Square (RLS) methods and the accuracy standard deviation of our algorithm is the closest to the Cramer–Rao bound (CRB).

People have to move between indoor and outdoor frequently in city scenarios. The global navigation satellite system signal cannot provide reliable indoor positioning services. To solve the problem, this article proposes a seamless positioning system based on an inverse global navigation satellite system signal, which can extend the global navigation satellite system service into the indoor scenario. In this method, a signal source is arranged at a key position in the room, and the inverse global navigation satellite system signal is transmitted to the global navigation satellite system receiver to obtain a preset positioning result. The indoor positioning service is continued with the inertial navigation system after leaving the key position. The inverse global navigation satellite system seamless positioning system proposed in this article can unify indoor and outdoor positioning using the same receiver. The receiver does not need to re-receive navigation information when the scene changes, which avoids the switching process. Through the design of signal layer coverage, the receiver is in a warm start state, and the users can quickly fix the position when the scenario changes, realizing quick access in a true sense. This enables the ordinary commercial global navigation satellite system receiver to obtain indoor positioning capability without modification, and the algorithm can perform accurate positioning indoors and outdoors without switching.

People generally find themselves lost while visiting a new indoor location because they are unaware of the building’s architecture, especially when it is a large one or a shopping mall. The Global Position System (GPS) does not work properly in the indoor environment because of the satellite signal attenuation. In this paper, to assist people in finding their path, a Li-Fi (Light-Fidelity) based Indoor Positioning System (IPS) is proposed. A framework is developed based on a Li-Fi LED lamp transmitter and a dongle receiver connected to an Android smartphone to decode the received sequence. The pathfinding graph-based algorithm is proceeded in a REST architecture by a Web service consulting the graph-path database both installed on a Raspberry pi 4. The proposed solution is a low cost and does not require any additional infrastructure. It is easy to implement in most indoor environments like hospitals, buildings, and campuses. A short survey of techniques and algorithms for indoor positioning and navigation with the help of smartphones is also presented.

Despite the great attention that the research community has paid to the creation of novel indoor positioning methods, a rather limited volume of works has focused on the confidence that Indoor Positioning Systems (IPS) assign to the position estimates that they produce. The concept of estimating, dynamically, the accuracy of the position estimates provided by an IPS has been sporadically studied in the literature of the field. Recently, this concept has started being studied as well in the context of outdoor positioning systems of Internet of Things (IoT) based on Low-Power Wide-Area Networks (LPWANs). What is problematic is that the consistent comparison of the proposed methods is quasi nonexistent: new methods rarely use previous ones as baselines; often, a small number of evaluation metrics are reported while different metrics are reported among different relevant publications, the use of open data is rare, and the publication of open code is absent. In this work, we present an open-source, reproducible benchmarking framework for evaluating and consistently comparing various methods of Dynamic Accuracy Estimation (DAE). This work reviews the relevant literature, presenting in a consistent terminology commonalities and differences and discussing baselines and evaluation metrics. Moreover, it evaluates multiple methods of DAE using open data, open code, and a rich set of relevant evaluation metrics. This is the first work aiming to establish the state of the art of methods of DAE determination in IPS and in LPWAN positioning systems, through an open, transparent, holistic, reproducible, and consistent evaluation of the methods proposed in the relevant literature.

A wide variety of semiautonomous systems are emerging in construction, defense, and agricultural applications. A UWB positioning system shows promise in improving navigational capabilities and safety of operations for these systems. This paper describes an outdoor UWB positioning system used to measure the position of the operators of a semiautonomous vehicle to improve safety of operations. A measurement campaign was conducted to collect experimental range errors for the system at distances from 2m to 40 m. The range errors are characterized and performance of the system is assessed in a static environment. A model is proposed for range errors and results are compared to experimental data. Measured position errors are compared to position errors generated by the model. A mobility model is proposed and performance of the positioning system in a mobile environment is assessed.

As the communication field continues to evolve, positioning technology has emerged as a crucial and valuable research avenue. Particularly, indoor positioning presents an innovative and trending direction in this technology. During the research process of indoor positioning, it is essential to consider the impact of various environmental disturbances on positioning accuracy. Additionally, efforts should be geared towards minimizing these environmental influences on the positioning system's performance and reducing the complexity of the positioning algorithm. The ideal scenario for indoor positioning involves using a relatively straightforward positioning algorithm to obtain results with high positioning accuracy. That said, it is a challenging balance to strike, given the intricate factors that influence the effectiveness of indoor positioning. This paper focuses on the Time Difference of Arrival (TDOA) localization method. We explore the TDOA-based triangle centroid algorithm, Fang's algorithm, and Chan's algorithm, all prominent methods in this field. Each of these approaches presents unique strengths and challenges, offering diverse solutions to address the complexity of indoor positioning. Their comparative analysis will provide insights into the development of efficient, accurate, and straightforward indoor positioning techniques.

LBS (location based service) is a very important issue in wireless communication network. Stable location determination can be more important issue for seamless services. GPS covers outdoor location perception but has a serious problem for indoor applications like in-building,tunnel, etc. For indoor positioning, many new positioning systems are developed and considered. RFID positioning system is one of strong candidate methods in indoor positioning since it provides simple indoor location detection and diverse compatibility with other existing positioning systems. In this paper, we propose an optimal reader configuration for performance enhancement without additional use of readers and signal strength amplification in RFID indoor positioning. And we prove RFID reader configuration is very important through many experimental results. A good configuration can give an enhanced recognition property. According to several kinds of selected reader configuration, we compare with performances and explain necessity of optimum reader configuration method.

Fingerprinting is one of the most reliable technique for indoor positioning. The concept of the fingerprinting is to compare the offline received signal strength (RSS) database and online RSS measurement to find the most similar reference point to represent the user position. However, fingerprinting is sensitive to the environmental changes, where the offline database is required to be updated. In this paper, we discuss the offline and online measurement mismatch when the transmission power is altered by the Wi-Fi access point. We propose dynamic adjustment (DA) algorithm to reduce the influence caused by the power variation. Experimental results validate that the proposed DA algorithm maintains the positioning accuracy under the power adjustment.