Hybrid Indoor Localization Using WiFi and UWB Technologies

The interest in indoor localization has been increasing in the last few years because of the numerous important applications related to the pervasive diffusion of mobile smart devices that could benefit from localization. Various wireless technologies are in use to perform indoor localization, and, among them, WiFi and UWB technologies are appreciated when robust and accurate localization is required. The major advantage of WiFi technology is that it is ubiquitous, and therefore it can be used to support localization without the introduction of a specific infrastructure. The major drawback of WiFi technology is that it does not often ensure sufficient accuracy. On the contrary, indoor localization based on UWB technology guarantees higher accuracy with increased robustness, but it requires the use of UWB-enabled devices and the deployment of specific infrastructures made of UWB beacons. Experimental results on the synergic use of WiFi and UWB technologies for localization are presented in this paper to show that hybrid approaches can be used to effectively to increase the accuracy of WiFi-based localization. Actually, presented experimental results show that the use of a small number of UWB beacons together with an ordinary WiFi infrastructure is sufficient to significantly increase the accuracy of localization and to make WiFi-based localization adequate to implement relevant location-based services and applications.

Download Full-text

An Algorithm for Accurate and Robust Indoor Localization Based on Nonlinear Programming

Electronics ◽

10.3390/electronics9010065 ◽

2020 ◽

Vol 9 (1) ◽

pp. 65 ◽

Cited By ~ 5

Author(s):

Stefania Monica ◽

Federico Bergenti

Keyword(s):

Nonlinear Programming ◽

Mobile Devices ◽

Optimization Problem ◽

Indoor Localization ◽

Wide Band ◽

Location Based Services ◽

Experimental Results ◽

Indoor Environments ◽

Anchor Nodes ◽

The Impact

The study of techniques to estimate the position of mobile devices with a high level of accuracy and robustness is essential to provide advanced location based services in indoor environments. An algorithm to enable mobile devices to estimate their positions in known indoor environments is proposed in this paper under the assumption that fixed anchor nodes are available at known locations. The proposed algorithm is specifically designed to be executed on the mobile device whose position is under investigation, and it allows the device to estimate its position within the environment by actively measuring distance estimates from the anchor nodes. In order to reduce the impact of the errors caused by the arrangement of the anchor nodes in the environment, the proposed algorithm first transforms the localization problem into an optimization problem, and then, it solves the derived optimization problem using techniques inspired by nonlinear programming. Experimental results obtained using ultra-wide band signaling are presented to assess the performance of the algorithm and to compare it with reference alternatives. The presented experimental results confirm that the proposed algorithm provides an increased level of accuracy and robustness with respect to two reference alternatives, regardless of the position of the anchor nodes.

Download Full-text

Indoor Visual Positioning Aided by CNN-Based Image Retrieval: Training-Free, 3D Modeling-Free

Sensors ◽

10.3390/s18082692 ◽

2018 ◽

Vol 18 (8) ◽

pp. 2692 ◽

Cited By ~ 17

Author(s):

Yujin Chen ◽

Ruizhi Chen ◽

Mengyun Liu ◽

Aoran Xiao ◽

Dewen Wu ◽

...

Keyword(s):

Image Retrieval ◽

Pose Estimation ◽

Indoor Localization ◽

Location Based Services ◽

Visual Localization ◽

Indoor Environments ◽

Deep Convolutional Neural Networks ◽

Accurate Localization ◽

Visual Odometer ◽

WiCLoc: A Novel CSI-based Fingerprint Localization System

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001420580033 ◽

2019 ◽

Vol 34 (04) ◽

pp. 2058003

Author(s):

Lei Zhang ◽

Yanjun Hu ◽

Yafeng Liu ◽

Jiaxiang Li ◽

Enjie Ding

Keyword(s):

Nearest Neighbor ◽

Indoor Localization ◽

Rapid Development ◽

Location Based Services ◽

Smart Devices ◽

Fingerprint Matching ◽

Wifi Networks ◽

Localization System ◽

Exhibition Hall ◽

Localization Systems

With the rapid development of smart devices and WiFi networks, WiFi-based indoor localization is becoming increasingly important in location-based services. Among various localization techniques, the fingerprint-based method has attracted much interest due to its high accuracy and low equipment requirement. Traditional fingerprint-based indoor localization systems mostly obtain positioning by measuring the received signal strength indicator (RSSI). However, the RSSI is affected by environmental influences, thereby limiting the precision of positioning. Therefore, we propose a new indoor fingerprint localization system based on channel state information (CSI). We adopt a novel method, in which the amplitude and phase of the CSI are fused to generate fingerprints in the training phase and apply a weighted [Formula: see text]-nearest neighbor (KNN) algorithm for fingerprint matching during the estimation phase. The system is validated in an exhibition hall and laboratory and we also compare the results of the proposed system with those of two CSI-based and an RSSI-based fingerprint localization systems. The results show that the proposed system achieves a minimum mean distance error of 0.85[Formula: see text]m in the exhibition hall and 1.28[Formula: see text]m in the laboratory, outperforming the other systems.

Download Full-text

Indoor Smartphone Localization Based on LOS and NLOS Identification

Sensors ◽

10.3390/s18113987 ◽

2018 ◽

Vol 18 (11) ◽

pp. 3987 ◽

Cited By ~ 6

Author(s):

Hyeon Jo ◽

Seungku Kim

Keyword(s):

Indoor Localization ◽

Estimation Error ◽

Distance Estimation ◽

Location Based Services ◽

Propagation Path ◽

Line Of Sight ◽

Arrival Angle ◽

Accurate Localization ◽

Communication Signal ◽

Localization Scheme

Accurate localization technology is essential for providing location-based services. Global positioning system (GPS) is a typical localization technology that has been used in various fields. However, various indoor localization techniques are required because GPS signals cannot be received in indoor environments. Typical indoor localization methods use the time of arrival, angle of arrival, or the strength of the wireless communication signal to determine the location. In this paper, we propose an indoor localization scheme using signal strength that can be easily implemented in a smartphone. The proposed algorithm uses a trilateration method to estimate the position of the smartphone. The accuracy of the trilateration method depends on the distance estimation error. We first determine whether the propagation path is line-of-sight (LOS) or non-line-of-sight (NLOS), and distance estimation is performed accordingly. This LOS and NLOS identification method decreases the distance estimation error. The proposed algorithm is implemented as a smartphone application. The experimental results show that distance estimation error is significantly reduced, resulting in accurate localization.

Download Full-text

A Survey of Smartphone-Based Indoor Positioning System Using RF-Based Wireless Technologies

Sensors ◽

10.3390/s20247230 ◽

2020 ◽

Vol 20 (24) ◽

pp. 7230

Author(s):

Santosh Subedi ◽

Jae-Young Pyun

Keyword(s):

Indoor Localization ◽

Indoor Positioning ◽

Performance Comparison ◽

Location Based Services ◽

Global Navigation Satellite Systems ◽

Smart Devices ◽

Positioning System ◽

Localization Accuracy ◽

Satellite Systems ◽

Indoor Positioning System

In recent times, social and commercial interests in location-based services (LBS) are significantly increasing due to the rise in smart devices and technologies. The global navigation satellite systems (GNSS) have long been employed for LBS to navigate and determine accurate and reliable location information in outdoor environments. However, the GNSS signals are too weak to penetrate buildings and unable to provide reliable indoor LBS. Hence, GNSS’s incompetence in the indoor environment invites extensive research and development of an indoor positioning system (IPS). Various technologies and techniques have been studied for IPS development. This paper provides an overview of the available smartphone-based indoor localization solutions that rely on radio frequency technologies. As fingerprinting localization is mostly accepted for IPS development owing to its good localization accuracy, we discuss fingerprinting localization in detail. In particular, our analysis is more focused on practical IPS that are realized using a smartphone and Wi-Fi/Bluetooth Low Energy (BLE) as a signal source. Furthermore, we elaborate on the challenges of practical IPS, the available solutions and comprehensive performance comparison, and present some future trends in IPS development.

Download Full-text

A 3D MAP AIDED DEEP LEARNING BASED INDOOR LOCALIZATION SYSTEM FOR SMART DEVICES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b4-2020-391-2020 ◽

2020 ◽

Vol XLIII-B4-2020 ◽

pp. 391-397

Author(s):

Y. Yang ◽

C. Toth ◽

D. Brzezinska

Keyword(s):

Deep Learning ◽

High Performance ◽

Large Scale ◽

Indoor Localization ◽

Indoor Positioning ◽

Location Based Services ◽

Smart Devices ◽

Visual Localization ◽

Indoor Location ◽

Semantic Labeling

Abstract. Indoor positioning technologies represent a fast developing field of research due to the rapidly increasing need for indoor location-based services (ILBS); in particular, for applications using personal smart devices. Recently, progress in indoor mapping, including 3D modeling and semantic labeling started to offer benefits to indoor positioning algorithms; mainly, in terms of accuracy. This work presents a method for efficient and robust indoor localization, allowing to support applications in large-scale environments. To achieve high performance, the proposed concept integrates two main indoor localization techniques: Wi-Fi fingerprinting and deep learning-based visual localization using 3D map. The robustness and efficiency of technique is demonstrated with real-world experiences.

Download Full-text

WiFi Fingerprint Localization for Emergency Response

Smart Technologies for Emergency Response and Disaster Management - Advances in Public Policy and Administration ◽

10.4018/978-1-5225-2575-2.ch003 ◽

2018 ◽

pp. 86-105 ◽

Cited By ~ 1

Author(s):

Yu Gu ◽

Min Peng ◽

Fuji Ren ◽

Jie Li

Keyword(s):

Radio Frequency ◽

Emergency Response ◽

Real World ◽

Indoor Localization ◽

State Of The Art ◽

Location Based Services ◽

Experimental Results ◽

Localization System ◽

Longest Common Subsequences ◽

Wifi Fingerprint

As a key enabler for diversified location-based services (LBSs) of pervasive computing, indoor WiFi fingerprint localization remains a hot topic for decades. For most of previous research, maintaining a stable Radio Frequency (RF) environment constitutes one implicit but basic assumption. However, there is little room for such assumption in real-world scenarios, especially for the emergency response. Therefore, we propose a novel solution (HED) for rapidly setting up an indoor localization system by harvesting from the bursting number of available wireless resources. Via extensive real-world experiments lasting for over 6 months, we show the superiority of our HED algorithm in terms of accuracy, complexity and stability over two state-of-the-art solutions that are also designed to resist the dynamics, i.e., FreeLoc and LCS (Longest Common Subsequences). Moreover, experimental results not only confirm the benefits brought by environmental dynamics, but also provide valuable investigations and hand-on experiences on the real-world localization system.

Download Full-text

Exploiting geotagged resources to spatial ranking by extending HITS algorithm

Computer Science and Information Systems ◽

10.2298/csis141015091t ◽

2015 ◽

Vol 12 (1) ◽

pp. 185-201 ◽

Cited By ~ 10

Author(s):

Nguyen Tri ◽

Jason Jung

Keyword(s):

Location Based Services ◽

Experimental Results ◽

Smart Devices ◽

Ranking Algorithm ◽

Weighting Schemes ◽

Novel Method ◽

Meaningful Relationships ◽

Spatial Ranking ◽

Hits Algorithm ◽

Font Color

With a large amount of geotagged resources from smart devices, it is important to provide users with intelligent location-based services. Particularly, in this work, we focus on spatial ranking service, which can retrieve a set of relevant resources with a certain tag. This paper designs ranking algorithm in order to find out a list of locations which are collected from geotagged resources on SNSs. As extending HITS algorithm [13], we propose a novel method (called GeoHITS) that can analyze an undirected 2-mode graph composed with a set of tags and a set of locations. Thereby, meaningful relationships between the locations and a set of tags are discovered by integrating several weighting schemes and HITS algorithm. To evaluate the proposed spatial ranking approach, we have shows the experimental results from the recommendation applications. This article has been corrected. Link to the correction <a href="http://dx.doi.org/10.2298/CSIS151203064E">10.2298/CSIS151203064E</a>

Download Full-text

Convex Optimization via Symmetrical Hölder Divergence for a WLAN Indoor Positioning System

Entropy ◽

10.3390/e20090639 ◽

2018 ◽

Vol 20 (9) ◽

pp. 639 ◽

Cited By ~ 5

Author(s):

Osamah Abdullah

Keyword(s):

Nearest Neighbor ◽

Indoor Localization ◽

Healthcare Providers ◽

Indoor Positioning ◽

Location Based Services ◽

Positioning System ◽

K Nearest Neighbor ◽

Multivariate Distributions ◽

Major Drawback ◽

Indoor Positioning System

Modern indoor positioning system services are important technologies that play vital roles in modern life, providing many services such as recruiting emergency healthcare providers and for security purposes. Several large companies, such as Microsoft, Apple, Nokia, and Google, have researched location-based services. Wireless indoor localization is key for pervasive computing applications and network optimization. Different approaches have been developed for this technique using WiFi signals. WiFi fingerprinting-based indoor localization has been widely used due to its simplicity, and algorithms that fingerprint WiFi signals at separate locations can achieve accuracy within a few meters. However, a major drawback of WiFi fingerprinting is the variance in received signal strength (RSS), as it fluctuates with time and changing environment. As the signal changes, so does the fingerprint database, which can change the distribution of the RSS (multimodal distribution). Thus, in this paper, we propose that symmetrical Hölder divergence, which is a statistical model of entropy that encapsulates both the skew Bhattacharyya divergence and Cauchy–Schwarz divergence that are closed-form formulas that can be used to measure the statistical dissimilarities between the same exponential family for the signals that have multivariate distributions. The Hölder divergence is asymmetric, so we used both left-sided and right-sided data so the centroid can be symmetrized to obtain the minimizer of the proposed algorithm. The experimental results showed that the symmetrized Hölder divergence consistently outperformed the traditional k nearest neighbor and probability neural network. In addition, with the proposed algorithm, the position error accuracy was about 1 m in buildings.

Download Full-text

DeepLocBox: Reliable Fingerprinting-Based Indoor Area Localization

Sensors ◽

10.3390/s21062000 ◽

2021 ◽

Vol 21 (6) ◽

pp. 2000

Author(s):

Marius Laska ◽

Jörg Blankenbach

Keyword(s):

Wireless Local Area Network ◽

Indoor Localization ◽

Local Area Network ◽

Success Probability ◽

Local Area ◽

Position Estimation ◽

Location Based Services ◽

Global Navigation Satellite Systems ◽

Area Network ◽

Floor Plan

Location-based services (LBS) have gained increasing importance in our everyday lives and serve as the foundation for many smartphone applications. Whereas Global Navigation Satellite Systems (GNSS) enable reliable position estimation outdoors, there does not exist any comparable gold standard for indoor localization yet. Wireless local area network (WLAN) fingerprinting is still a promising and widely adopted approach to indoor localization, since it does not rely on preinstalled hardware but uses the existing WLAN infrastructure typically present in buildings. The accuracy of the method is, however, limited due to unstable fingerprints, etc. Deep learning has recently gained attention in the field of indoor localization and is also utilized to increase the performance of fingerprinting-based approaches. Current solutions can be grouped into models that either estimate the exact position of the user (regression) or classify the area (pre-segmented floor plan) or a reference location. We propose a model, DeepLocBox (DLB), that offers reliable area localization in multi-building/multi-floor environments without the prerequisite of a pre-segmented floor plan. Instead, the model predicts a bounding box that contains the user’s position while minimizing the required prediction space (size of the box). We compare the performance of DLB with the standard approach of neural network-based position estimation and demonstrate that DLB achieves a gain in success probability by 9.48% on a self-collected dataset at RWTH Aachen University, Germany; by 5.48% for a dataset provided by Tampere University of Technology (TUT), Finland; and by 3.71% for the UJIIndoorLoc dataset collected at Jaume I University (UJI) campus, Spain.

Download Full-text