Special Issue "Indoor Localization: Technologies and Challenges"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: 31 October 2020.

Special Issue Editor

Dr. Raúl Montoliu
Website
Guest Editor
Institute of New Imaging Technologies, Jaume I University, 12071 Castellón, Spain
Interests: Indoor localization; machine learning; game AI

Special Issue Information

Dear Colleagues,

Many technologies are under development for Indoor Positioning and Indoor Navigation. A great variety of promising technical solutions has been proposed, which are suitable for various application areas and use cases, e.g., IoT, home, public areas, or industrial environments, but there is no designated indoor positioning system that meets expectations as GNSS outdoors. These technologies operate using radio waves, acoustic, optical, infrared or radar signals, signal strength, magnetic sensors, and inertial sensors, among others.

This Special Issue encourages authors from academia and industry to submit new research results regarding technological innovations and novel applications for the indoor positioning and navigation of sensor networks. We also welcome comprehensive reviews on well-established and relatively mature technologies, demonstrating the technical performance, potentials, and limitations of these technical solutions.

Topics of interest include but are not limited to the following areas:

  • Localization technologies: TOF, TDOA, AoA, ADoA, RSSI, fingerprinting, dead reckoning;
  • Sensors and sensory systems: acoustic, RF, UWB, optical, magnetic, IMU;
  • Sensor fusion, filtering, and error mitigation;
  • Hybrid positioning;
  • Signal processing for indoor localization;
  • Machine learning in indoor localization;
  • Applications and localization services (ambient assisted living, robotics, etc.);
  • Unconventional solutions;
  • News trends on indoor localization.

Prof. Dr. Raúl Montoliu
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (6 papers)

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Research

Open AccessArticle
Assessment of Anchors Constellation Features in RSSI-Based Indoor Positioning Systems for Smart Environments
Electronics 2020, 9(6), 1026; https://doi.org/10.3390/electronics9061026 - 21 Jun 2020
Abstract
In this paper, we assess the features of a rectangular constellation of four anchors on the position estimation accuracy of a mobile tag, operating under the IEEE 802.15.4 specifications. Each anchor implements a smart antenna with eight switched beams, which is capable to [...] Read more.
In this paper, we assess the features of a rectangular constellation of four anchors on the position estimation accuracy of a mobile tag, operating under the IEEE 802.15.4 specifications. Each anchor implements a smart antenna with eight switched beams, which is capable to collect Received Signal Strength Indicator (RSSI) data, exploited to estimate the mobile tag position within a room. We also aim at suggesting a deployment criterion, providing the discussion of the best trade-off between system complexity and positioning accuracy. The assessment validation was conducted experimentally by implementing anchor constellations with different mesh sizes in the same room. Mean accuracies spanning from 0.32 m to 0.7 m on a whole 7.5 m × 6 m room were found by varying the mesh area from 1.19 m2 to 17 m2, respectively. Full article
(This article belongs to the Special Issue Indoor Localization: Technologies and Challenges)
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Open AccessFeature PaperArticle
Indoor Positioning for Monitoring Older Adults at Home: Wi-Fi and BLE Technologies in Real Scenarios
Electronics 2020, 9(5), 728; https://doi.org/10.3390/electronics9050728 - 28 Apr 2020
Cited by 1
Abstract
This paper presents our experience on a real case of applying an indoor localization system for monitoring older adults in their own homes. Since the system is designed to be used by real users, there are many situations that cannot be controlled by [...] Read more.
This paper presents our experience on a real case of applying an indoor localization system for monitoring older adults in their own homes. Since the system is designed to be used by real users, there are many situations that cannot be controlled by system developers and can be a source of errors. This paper presents some of the problems that arise when real non-expert users use localization systems and discusses some strategies to deal with such situations. Two technologies were tested to provide indoor localization: Wi-Fi and Bluetooth Low Energy. The results shown in the paper suggest that the Bluetooth Low Energy based one is preferable in the proposed task. Full article
(This article belongs to the Special Issue Indoor Localization: Technologies and Challenges)
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Open AccessFeature PaperArticle
Effects of the Body Wearable Sensor Position on the UWB Localization Accuracy
Electronics 2019, 8(11), 1351; https://doi.org/10.3390/electronics8111351 - 14 Nov 2019
Cited by 3
Abstract
Over the years, several Ultrawideband (UWB) localization systems have been proposed and evaluated for accurate estimation of the position for pedestrians. However, most of them are evaluated for a particular wearable sensor position; hence, the accuracy obtained is subject to a given wearable [...] Read more.
Over the years, several Ultrawideband (UWB) localization systems have been proposed and evaluated for accurate estimation of the position for pedestrians. However, most of them are evaluated for a particular wearable sensor position; hence, the accuracy obtained is subject to a given wearable sensor position. This paper is focused on studying the effects of body wearable sensor positions i.e., chest, arm, ankle, wrist, thigh, forehead, and hand, on the localization accuracy. According to our results, the forehead and the chest provide the best and worst body sensor location for tracking a pedestrian, respectively. With the wearable sensor at the forehead and chest position, errors lower than 0.35 m (90th percentile) and 4 m can be obtained, respectively. The reason for such a contrast in the performance lies in the fact that, in non-line-of-sight (NLOS) situations, the chest generates the highest multipath of any part of the human body. Thus, the large errors obtained arise due to the signal arriving at the target wearable sensor by multiple reflections from interacting objects in the environment rather than by direct line-of-sight (LOS) or creeping wave propagation mechanism. Full article
(This article belongs to the Special Issue Indoor Localization: Technologies and Challenges)
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Open AccessFeature PaperArticle
SDR Based Indoor Beacon Localization Using 3D Probabilistic Multipath Exploitation and Deep Learning
Electronics 2019, 8(11), 1323; https://doi.org/10.3390/electronics8111323 - 10 Nov 2019
Cited by 1
Abstract
Wireless indoor positioning systems (IPS) are ever-growing as traditional global positioning systems (GPS) are ineffective due to non-line-of-sight (NLoS) signal propagation. In this paper, we present a novel approach to learning three-dimensional (3D) multipath channel characteristics in a probabilistic manner for providing high [...] Read more.
Wireless indoor positioning systems (IPS) are ever-growing as traditional global positioning systems (GPS) are ineffective due to non-line-of-sight (NLoS) signal propagation. In this paper, we present a novel approach to learning three-dimensional (3D) multipath channel characteristics in a probabilistic manner for providing high performance indoor localization of wireless beacons. The proposed system employs a single triad dipole vector sensor (TDVS) for polarization diversity, a deep learning model deemed the denoising autoencoder to extract unique fingerprints from 3D multipath channel information, and a probabilistic k-nearest-neighbor (PkNN) to exploit the 3D multipath characteristics. The proposed system is the first to exploit 3D multipath channel characteristics for indoor wireless beacon localization via vector sensing methodologies, a software defined radio (SDR) platform, and multipath channel estimation. Full article
(This article belongs to the Special Issue Indoor Localization: Technologies and Challenges)
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Open AccessArticle
Comparison of CNN Applications for RSSI-Based Fingerprint Indoor Localization
Electronics 2019, 8(9), 989; https://doi.org/10.3390/electronics8090989 - 04 Sep 2019
Cited by 4
Abstract
The intelligent use of deep learning (DL) techniques can assist in overcoming noise and uncertainty during fingerprinting-based localization. With the rise in the available computational power on mobile devices, it is now possible to employ DL techniques, such as convolutional neural networks (CNNs), [...] Read more.
The intelligent use of deep learning (DL) techniques can assist in overcoming noise and uncertainty during fingerprinting-based localization. With the rise in the available computational power on mobile devices, it is now possible to employ DL techniques, such as convolutional neural networks (CNNs), for smartphones. In this paper, we introduce a CNN model based on received signal strength indicator (RSSI) fingerprint datasets and compare it with different CNN application models, such as AlexNet, ResNet, ZFNet, Inception v3, and MobileNet v2, for indoor localization. The experimental results show that the proposed CNN model can achieve a test accuracy of 94.45% and an average location error as low as 1.44 m. Therefore, our CNN model outperforms conventional CNN applications for RSSI-based indoor positioning. Full article
(This article belongs to the Special Issue Indoor Localization: Technologies and Challenges)
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Open AccessArticle
Fine Frequency Acquisition Scheme in Weak Signal Environment for a Communication and Navigation Fusion System
Electronics 2019, 8(8), 829; https://doi.org/10.3390/electronics8080829 - 25 Jul 2019
Cited by 2
Abstract
A novel communication and navigation fusion system (CNFS) was developed to realized high accuracy positioning in constrained conditions. Communication and navigation fusion signal transmitted by base stations are in the same time and frequency band but are allocated different power levels. The positioning [...] Read more.
A novel communication and navigation fusion system (CNFS) was developed to realized high accuracy positioning in constrained conditions. Communication and navigation fusion signal transmitted by base stations are in the same time and frequency band but are allocated different power levels. The positioning receiver of CNFS requires signal coverage of at least four base stations to realize positioning. The improvement of receiver sensitivity is an important way to expand signal coverage of base station. As an essential stage of signal processing in CNFS positioning receiver, signal acquisition requires a trade-off between improvement of acquisition frequency accuracy and reduction in computational load. A new acquisition algorithm called PMF-FC-BA-FFT method is proposed to acquire the carrier frequency accurately with lower computational load in a weak signal environment. The received signal is firstly filtered by partially matched filters (PMF) with local replica pseudorandom noise (PRN) sequences being coefficients to strip off the PRN code in the signal. Frequency compensation (FC) was performed to avoid the large attenuation in block accumulation (BA) and generate a series of signals with a small frequency offset step. Block accumulation was then executed. Finally, the acquisition detection was performed based on a series of fast Fourier transformation (FFT) outputs to obtain acquisition results with fine frequency estimation. Simulations and experimental tests results show that the proposed method can realize high accuracy frequency acquisition in a weak signal environment with fewer computational resources compared with existing acquisition methods. Full article
(This article belongs to the Special Issue Indoor Localization: Technologies and Challenges)
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