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Localising Sensors through Wireless Communication

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (20 February 2023) | Viewed by 11659

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Special Issue Editors

Prof. Dr. Maarten Weyn

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Guest Editor

IDLab – Faculty of Applied Engineering, University of Antwerp—IMEC, Sint-Pietersvliet 7, 2000 Antwerp, Belgium
Interests: wireless communication; localisation and tracking; low-power embedded systems
Special Issues, Collections and Topics in MDPI journals

Dr. Rafael Berkvens

E-Mail Website
Guest Editor

IDLab—Faculty of Applied Engineering, University of Antwerp, Imec, Sint-Pietersvliet 7, 2000 Antwerp, Belgium
Interests: integrated communication and sensing; passive sensing; signals of opportunity; sustainability
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Eli De Poorter

E-Mail Website
Guest Editor

IDLab, Department of Information Technology, Ghent University-imec, Technologiepark-Zwijnaarde 126, B-9052 Gent, Belgium
Interests: wireless communication; indoor localisation; Internet of Things; machine learning for wireless networks; network protocols for low-power constrained devices
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The localisation of mobile devices often uses their wireless communication channels to estimate or aid the estimation of their location. Well-known examples include triangulation in cellular networks, fingerprinting in Wi-Fi networks, or proximity detection with Bluetooth radios. These concepts are still of interest, as they allow the finetuning of location estimation through advanced methods in artificial intelligence, Bayesian filtering, and multimodality. More recently, novel technologies have become widely available: ultrawideband, low-power wide area networks, and vehicular communication. These are now being investigated with proven approaches, and with smart approaches that incorporate the unique features of those novel technologies.

Certain research questions remain difficult to answer: how accurately can a location be estimated? How often can the location be updated? How much or little energy is necessary for localization purposes? How can privacy be preserved?

This Special Issue aims to report high-quality research in recent advances in localising sensors through wireless communication.

Prof. Dr. Maarten Weyn
Dr. Rafael Berkvens
Prof. Dr. Eli De Poorter
Guest Editors

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Keywords

Wireless communication
Wireless sensor networks
Localisation of mobile sensors
Positioning of mobile sensors
Location tracking of mobile sensors
Bayesian filtering for location tracking
Machine learning for location estimates
Artificial intelligence for location estimates
Multimodal approach for localisation
Ultrawideband
Low-power wide area networks
Cellular networks, 4G, 5G, and 6G
Cellular vehicle to everything
Wi-Fi
Bluetooth
License-free networks

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Published Papers (4 papers)

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Research

28 pages, 11874 KiB

Open AccessArticle

Speaker Counting Based on a Novel Hive Shaped Nested Microphone Array by WPT and 2D Adaptive SRP Algorithms in Near-Field Scenarios

by Ali Dehghan Firoozabadi, Pablo Adasme, David Zabala-Blanco, Pablo Palacios Játiva and Cesar Azurdia-Meza

Sensors 2023, 23(9), 4499; https://doi.org/10.3390/s23094499 - 5 May 2023

Viewed by 1740

Abstract

Speech processing algorithms, especially sound source localization (SSL), speech enhancement, and speaker tracking are considered to be the main fields in this application. Most speech processing algorithms require knowing the number of speakers for real implementation. In this article, a novel method for estimating the number of speakers is proposed based on the hive shaped nested microphone array (HNMA) by wavelet packet transform (WPT) and 2D sub-band adaptive steered response power (SB-2DASRP) with phase transform (PHAT) and maximum likelihood (ML) filters, and, finally, the agglomerative classification and elbow criteria for obtaining the number of speakers in near-field scenarios. The proposed HNMA is presented for aliasing and imaging elimination and preparing the proper signals for the speaker counting method. In the following, the Blackman–Tukey spectral estimation method is selected for detecting the proper frequency components of the recorded signal. The WPT is considered for smart sub-band processing by focusing on the frequency bins of the speech signal. In addition, the SRP method is implemented in 2D format and adaptively by ML and PHAT filters on the sub-band signals. The SB-2DASRP peak positions are extracted on various time frames based on the standard deviation (SD) criteria, and the final number of speakers is estimated by unsupervised agglomerative clustering and elbow criteria. The proposed HNMA-SB-2DASRP method is compared with the frequency-domain magnitude squared coherence (FD-MSC), i-vector probabilistic linear discriminant analysis (i-vector PLDA), ambisonics features of the correlational recurrent neural network (AF-CRNN), and speaker counting by density-based classification and clustering decision (SC-DCCD) algorithms on noisy and reverberant environments, which represents the superiority of the proposed method for real implementation. Full article

(This article belongs to the Special Issue Localising Sensors through Wireless Communication)

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19 pages, 1151 KiB

Open AccessArticle

Experimental Evaluation of Sensor Fusion of Low-Cost UWB and IMU for Localization under Indoor Dynamic Testing Conditions

by Chengkun Liu, Tchamie Kadja and Vamsy P. Chodavarapu

Sensors 2022, 22(21), 8156; https://doi.org/10.3390/s22218156 - 25 Oct 2022

Cited by 4 | Viewed by 3182

Abstract

Autonomous systems usually require accurate localization methods for them to navigate safely in indoor environments. Most localization methods are expensive and difficult to set up. In this work, we built a low-cost and portable indoor location tracking system by using Raspberry Pi 4 computer, ultra-wideband (UWB) sensors, and inertial measurement unit(s) (IMU). We also developed the data logging software and the Kalman filter (KF) sensor fusion algorithm to process the data from a low-power UWB transceiver (Decawave, model DWM1001) module and IMU device (Bosch, model BNO055). Autonomous systems move with different velocities and accelerations, which requires its localization performance to be evaluated under diverse motion conditions. We built a dynamic testing platform to generate not only the ground truth trajectory but also the ground truth acceleration and velocity. In this way, our tracking system’s localization performance can be evaluated under dynamic testing conditions. The novel contributions in this work are a low-cost, low-power, tracking system hardware–software design, and an experimental setup to observe the tracking system’s localization performance under different dynamic testing conditions. The testing platform has a 1 m translation length and 80 μm of bidirectional repeatability. The tracking system’s localization performance was evaluated under dynamic conditions with eight different combinations of acceleration and velocity. The ground truth accelerations varied from 0.6 to 1.6 m/s² and the ground truth velocities varied from 0.6 to 0.8 m/s. Our experimental results show that the location error can reach up to 50 cm under dynamic testing conditions when only relying on the UWB sensor, with the KF sensor fusion of UWB and IMU, the location error decreases to 13.7 cm. Full article

(This article belongs to the Special Issue Localising Sensors through Wireless Communication)

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19 pages, 2764 KiB

Open AccessArticle

Support Vector Regression for Mobile Target Localization in Indoor Environments

by Satish R. Jondhale, Vijay Mohan, Bharat Bhushan Sharma, Jaime Lloret and Shashikant V. Athawale

Sensors 2022, 22(1), 358; https://doi.org/10.3390/s22010358 - 4 Jan 2022

Cited by 34 | Viewed by 2922

Abstract

Trilateration-based target localization using received signal strength (RSS) in a wireless sensor network (WSN) generally yields inaccurate location estimates due to high fluctuations in RSS measurements in indoor environments. Improving the localization accuracy in RSS-based systems has long been the focus of a substantial amount of research. This paper proposes two range-free algorithms based on RSS measurements, namely support vector regression (SVR) and SVR + Kalman filter (KF). Unlike trilateration, the proposed SVR-based localization scheme can directly estimate target locations using field measurements without relying on the computation of distances. Unlike other state-of-the-art localization and tracking (L&T) schemes such as the generalized regression neural network (GRNN), SVR localization architecture needs only three RSS measurements to locate a mobile target. Furthermore, the SVR based localization scheme was fused with a KF in order to gain further refinement in target location estimates. Rigorous simulations were carried out to test the localization efficacy of the proposed algorithms for noisy radio frequency (RF) channels and a dynamic target motion model. Benefiting from the good generalization ability of SVR, simulation results showed that the presented SVR-based localization algorithms demonstrate superior performance compared to trilateration- and GRNN-based localization schemes in terms of indoor localization performance. Full article

(This article belongs to the Special Issue Localising Sensors through Wireless Communication)

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19 pages, 384 KiB

Open AccessArticle

Device Free Detection in Impulse Radio Ultrawide Bandwidth Systems

by Waqas Bin Abbas, Fuhu Che, Qasim Zeeshan Ahmed, Fahd Ahmed Khan and Temitope Alade

Sensors 2021, 21(9), 3255; https://doi.org/10.3390/s21093255 - 8 May 2021

Cited by 7 | Viewed by 2222

Abstract

In this paper, an analytical framework is presented for device detection in an impulse radio (IR) ultra-wide bandwidth (UWB) system and its performance analysis is carried out. The Neyman–Pearson (NP) criteria is employed for this device-free detection. Different from the frequency-based approaches, the proposed detection method utilizes time domain concepts. The characteristic function (CF) is utilized to measure the moments of the presence and absence of the device. Furthermore, this method is easily extendable to existing device-free and device-based techniques. This method can also be applied to different pulse-based UWB systems which use different modulation schemes compared to IR-UWB. In addition, the proposed method does not require training to measure or calibrate the system operating parameters. From the simulation results, it is observed that an optimal threshold can be chosen to improve the ROC for UWB system. It is shown that the probability of false alarm,

P_{F A}

, has an inverse relationship with the detection threshold and frame length. Particularly, to maintain

P_{F A} < 10^{- 5}

for a frame length of 300 ns, it is required that the threshold should be greater than

2.2

. It is also shown that for a fix

P_{F A}

, the probability of detection

P_{D}

increases with an increase in interference-to-noise ratio (INR). Furthermore,

P_{D}

approaches 1 for INR

> - 2

dB even for a very low

P_{F A}

i.e.,

P_{F A} = 1 \times 10^{- 7}

. It is also shown that a 2 times increase in the interference energy results in a 3 dB improvement in INR for a fixed

P_{F A} = 0.1

and

P_{D} = 0.5

. Finally, the derived performance expressions are corroborated through simulation. Full article

(This article belongs to the Special Issue Localising Sensors through Wireless Communication)

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