Preliminary Study on Indoor Localization using Smartphone-Based IEEE 802.11mc

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 & 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 & WCCG method demonstrate good tolerance against ranging errors. Moreover, the computational cost and positioning accuracy of CbT & 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).

Indoor localization has recently and significantly attracted the interest of the research community mainly due to the fact that Global Navigation Satellite Systems (GNSSs) typically fail in indoor environments. In the last couple of decades, there have been several works reported in the literature that attempt to tackle the indoor localization problem. However, most of this work is focused solely on two-dimensional (2D) localization, while very few papers consider three dimensions (3D). There is also a noticeable lack of survey papers focusing on 3D indoor localization; hence, in this paper, we aim to carry out a survey and provide a detailed critical review of the current state of the art concerning 3D indoor localization including geometric approaches such as angle of arrival (AoA), time of arrival (ToA), time difference of arrival (TDoA), fingerprinting approaches based on Received Signal Strength (RSS), Channel State Information (CSI), Magnetic Field (MF) and Fine Time Measurement (FTM), as well as fusion-based and hybrid-positioning techniques. We provide a variety of technologies, with a focus on wireless technologies that may be utilized for 3D indoor localization such as WiFi, Bluetooth, UWB, mmWave, visible light and sound-based technologies. We critically analyze the advantages and disadvantages of each approach/technology in 3D localization.

As the number of smartphone users exploded, the demand for Location-Based Service (LBS) has increased. It is important for the LBS to specify the user location by utilizing the sensor built in the smartphone. Unlike outdoor localization, which can employ GPS, there are many challenging issues in indoor localization including non-line-of-sight (NLOS) and multipath effect. In our paper, we focus on Wi-Fi Fine Timing Measurement (FTM) which is a new function of the Android Pie Operating System (OS). We propose line-of-sight (LOS) identification algorithms applicable to Wi-Fi FTM and apply these algorithms to indoor localization based on multilateration methods. We utilize a hypothesis test framework and Support Vector Machine (SVM) to identify LOS signals. We divide LOS/NLOS signals as low and high-quality signals according to the degree of multipath error. We achieve high-quality signals identification rate of 92.4% on average in the sample size 99 and of 78.3% on average in the sample size 29. Therefore, we obtain a 24.4% localization performance improvement compared to the perfect LOS detector by using only high-quality signals to localization.

More and more applications of location-based services lead to the development of indoor positioning technology. Wi-Fi-based indoor localization has been attractive due to its extensive distribution and low cost properties. IEEE 802.11-2016 now includes a Wi-Fi Fine Time Measurement (FTM) protocol which provides a more robust approach for Wi-Fi ranging between the mobile terminal and Wi-Fi access point (AP). To improve the positioning accuracy, in this paper, we propose a robust dead reckoning algorithm combining the results of Wi-Fi FTM and multiple sensors (DRWMs). A real-time Wi-Fi ranging model is built which can effectively reduce the Wi-Fi ranging errors, and then a multisensor multi-pattern-based dead reckoning is presented. In addition, the Unscented Kalman filter (UKF) is applied to fuse the results of Wi-Fi ranging model and multiple sensors. The experiment results show that the proposed DRWMs algorithm can achieve accurate localization performance in line-of-sight/non-line-of-sight (LOS)/(NLOS) mixed indoor environment. Compared with the traditional Wi-Fi positioning method and the traditional dead reckoning method, the proposed algorithm is more stable and has better real-time performance for indoor positioning.

Sensors-based and radio frequency (RF)-based indoor localization technology is one of the keys in location-based services. The IEEE 802.11-2016 introduced the Wi-Fi fine timing measurement (FTM) protocol, which provides a new approach for Wi-Fi-based indoor localization. However, Wi-Fi signals are susceptible to complex indoor environments. To improve the positioning accuracy and stability, an enhanced particle filter (PF) with two different state update strategies, a new criterion for divergence monitoring and rapid re-initialization is proposed to integrate the advantages of pedestrian dead reckoning (PDR) and Wi-Fi FTM. In addition, an adaptive tilt compensation is proposed to improve real-time heading estimation of conventional PDR, and the Wi-Fi FTM outliers are detected by displacement estimation of the PDR. The experimental results show that the proposed PF has better localization performance than the single source positioning methods in a typical indoor scenario. The accuracy of final localization is within 1 m in 86.7% of the dynamic cases and the average calculation time is less than 0.5 s when the number of particles is 2000.

Abstract. Modern mobile devices offer advanced capabilities for navigation and Location-Based Services (LBS). This study investigates the use of Ultra-Wideband (UWB) and Wi-Fi Round-Trip Time (RTT) measurements for pedestrian localization across various scenarios. We analyze the ranging accuracy of these techniques and evaluate positioning performance using different fusion algorithms. Our proposed collaborative positioning (CP) methodology enhances localization accuracy for user groups within a defined area. Results demonstrate significant improvements in position trueness, with UWB and Wi-Fi RTT achieving enhancements of 74% and 54%, respectively. Additionally, our localization algorithm, leveraging a Peer-to-Infrastructure (P2I) and Peer-to-Peer (P2P) hybrid configuration, increases anchor availability by up to 10%. Even in short-duration anchor loss scenarios (P2P-only), position trueness improves by up to 53%. These findings highlight the potential of UWB and Wi-Fi RTT in real-world pedestrian localization, particularly for urban navigation, smart mobility applications, and emergency response systems.

With the invent of GPS and related technologies, outdoor navigation is simply possible with great accuracy nowadays. But we are still in need of an efficient, reliable and cheaper technology for indoor navigation. In this paper we put forward the idea of an android based application which enables efficient indoor navigation and tracks the exact location of people inside large buildings. Though different technologies like WiFi, Bluetooth, RFID e.t.c, are available for indoor navigation and location tracking, the main problem is that the implementation and maintenance costs are very high. Also, the change in climatic condition affects the working and degrades its performance. In this work, we incorporate the innovative idea of magnetic field identification technology which uses the naturally occurring magnetic fields inside the concrete buildings with simplified magnetic map matching algorithm and the idea from Route boxer algorithm for effective indoor positioning and navigation. A Smart phone with magnetic field sensor and accelerometer enables the application to provide the best indoor navigation facility to the end users.

Non line of sight (NLOS) conditions significantly degrade the accuracy of Wi-Fi-Based indoor positioning systems (IPS). Although round-trip time (RTT) is generally more reliable than the received signal strength indicator (RSSI), it remains vulnerable to attenuation and multipath; moreover, dense RTT-compatible AP deployments may threaten scalability. This paper proposes a fingerprinting-based IPS that achieves high accuracy in NLOS scenarios, while remaining scalable and lightweight for Internet of Things (IoT) devices. By combining RTT, its standard deviation, and RSSI, accurate position estimates are obtained with only two RTT requests, reaching 93% accuracy and a 62 cm root mean squared error (RMSE) despite harsh conditions.

Location based service (LBS) cannot be realized unless the location of the user is available. For indoor LBS, indoor positioning must be utilized and many researchers have been working on indoor positioning and tracking. For example, extended Kalman filter (EKF) was exploited in Bluetooth based indoor positioning. Nowadays, WLAN (wireless local area network) is available virtually everywhere. Thus, WLAN based indoor positioning and tracking is more economical than Bluetooth based ones. The main purpose of this paper is to propose a new WLAN based EKF indoor tracking method by extending existing Bluetooth based EKF positioning method. Experimental results comparing our method with other previous methods are discussed.

High-precision indoor localization and tracking are essential requirements for the safe navigation and task execution of autonomous mobile robots. Despite the growing importance of mobile robots in various areas, achieving precise indoor localization remains challenging due to signal interference, multipath propagation, and complex indoor layouts. In this work, we present the first comprehensive study comparing the accuracy of Bluetooth low energy (BLE), WiFi, and ultra wideband (UWB) technologies for the indoor localization of mobile robots under various circumstances. In the performed experiments, the error margin of the WiFi-based systems reached 608.7 cm, which is not tolerable for most applications. As a commonly used technology in the existing tracking systems, the accuracy of BLE-based systems is at least 44.12% better than that of WiFi-based systems. The error margin of the BLE-based system in tracking static and mobile robots was 191.7 cm and 340.1 cm, respectively. The experiments showed that even with a limited number of UWB anchors, the system provides acceptable accuracy for tracking the mobile robots. Using only four UWB beacons in an environment of about 431 m2 area, the maximum error margin of detected positions by the UWB-based tracking system remained below 13.1 cm and 28.9 cm on average for the static and mobile robots, respectively. This error margin is 88.05% lower than that of the BLE-based system and 93.27% lower than that of the WiFi-based system on average. The high tracking precision, the need for a lower number of anchors, and the decreasing hardware costs point out that UWB will be the dominating technology in indoor tracking systems in the near future.

A fundamental goal of indoor localisation technology is to achieve the milestone of combining minimal cost with accuracy sufficient enough for general consumer applications. To achieve this, current indoor positioning systems need either extensive calibration or expensive hardware. Moreover, very few systems built so far have addressed floor determination in multi-story buildings. In this paper, we explain a Wi-fi-based indoor localisation, tracking and navigation system for multi-story buildings called Locus. Locus determines a device’s floor as well as location on that floor using existing knowledge of infrastructure, and without requiring any calibration or proprietary hardware. It is an inexpensive solution with minimum set-up and maintenance expenses, is scalable, readily deployable and robust to environmental changes. Experimental results in three different buildings spanning multiple floors show that it can determine the floor with 95.33% accuracy and the location on the floor with an error of 6.49 m on an average in real-life practical environments. We also demonstrate its utility via two location-based applications for indoor navigation and tracking in emergency scenarios.

Empirical models are most widely used for path loss predictions because they are simple, easy to use, and require less computational efficiency when compared to deterministic models. A number of empirical path loss models have been developed for efficient radio network planning and optimization in different types of propagation environments. However, data that prove the suitability of these models for path loss predictions in a typical university campus propagation environment are yet to be reported in the literature. In this data article, empirical prediction models are comparatively assessed using the path loss data measured and predicted for a university campus environment. Field measurement campaigns are conducted at 1800 MHz radio frequency to log the actual path losses along three major routes within the campus of Covenant University, Nigeria. Path loss values are computed along the three measurement routes based on four popular empirical path loss models (Okumura-Hata, COST 231, ECC-33, and Egli). Datasets containing measured and predicted path loss values are presented in a spreadsheet file, which is attached to this data article as supplementary material. Path loss prediction data of the empirical models are compared to those of the measured path loss using first-order statistics, boxplot representations, tables, and graphs. In addition, correlation analysis, Analysis of Variance (ANOVA), and multiple comparison post-hoc tests are performed. The prediction accuracies of the empirical models are evaluated based on Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Standard Error Deviation (SED). In conclusion, the high-resolution path loss prediction datasets and the rich data exploration provided in this data article will help radio network engineers and academic researchers to determine the empirical model that is most suitable for path loss prediction in a typical university campus environment.

Modern smartphones and location-based services and apps are poised to transform our daily life. However, current smartphone-based localization solutions are limited mainly to outdoor, mostly missing practical, robust and accurate indoor location solutions. Despite significant efforts on indoor localization in both academia and industry in the past two decades, highly accurate and practical smartphone-based indoor localization remains an open problem. To enable indoor location-based services (ILBS), e.g., step-by-step navigation for the Blind and visually impaired, there are several stringent requirements: highly accurate (foot-level); no additional hardware components or extensions on users’ smartphones; scalable to massive concurrent users. Current GPS, Radio RSS (e.g. Wi-Fi, Bluetooth, ZigBee), or Fingerprinting based solutions can only achieve meter-level or room-level accuracy. In this paper, we propose a practical and accurate solution that fills the long-lasting gap of smartphone-based fine-grained indoor localization. Specifically, we design and implement an indoor localization ecosystem Guoguo. Guoguo consists of an anchor network with a coordination protocol to transmit modulated localization beacons using high-band acoustic signals, a realtime processing app in a smartphone, and a backend server for indoor contexts and location-based services. We further propose approaches to improve its coverage, accuracy, and location update rate with low-power consumption. Our prototype shows centimeter-level localization accuracy in several typical indoor environments. Such precise indoor localization is expected to have high impact in the future ILBS and our daily activities.

Global positioning system (GPS) tracks a desired location and display it on map. However, GPS may not work properly for indoor positioning, when we are concerned with an accurate location of a person inside a building. Therefore, to determine an accurate indoor positioning based on smart devices a scheme is proposed and called iLocation. iLocation measures the location by obtaining and computing values from an accelerometer and a barometer of a smart device. iLocation uses MySQL to maintain records of the computed indoor positions. Such positions are displayed on Google Maps with its images for the verification. iLocation is tested on various real time scenarios and experimental results shows the accuracy, which is better than other existing approaches.

In this paper, a quasi-Yagi antenna for an indoor location tracking system is proposed. The performance of the proposed antenna was verified by testing it using an indoor location tracking system. To improve the bandwidth and gain, two parasitic directors were added near the dipole. The performance verification of the proposed antenna is explained, along with a performance comparison of the VSWR (voltage standing wave ratio) radiation pattern and the realized gain. The proposed antenna was connected to an NVA-R661 module of Xethru Inc. for indoor location tracking. The proposed antenna exhibited a wide bandwidth of 4.36 GHz by satisfying a VSWR ≤ 2 from 5.03 to 9.39 GHz, the maximum gain was 6.46 dBi in the 8 GHz band. The radiation pattern exhibited a good directivity characteristic within the proposed band. The location tracking result of a moving target clearly describes the route of the target along a moving line.