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Sensors and Systems for Indoor Positioning

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

Deadline for manuscript submissions: closed (15 July 2021) | Viewed by 30455

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Special Issue Editors

Prof. Dr. Riccardo Carotenuto

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Guest Editor

Department of Information Engineering Infrastructures and Sustainable Energy (DIIES), “Mediterranea” University, 89122 Reggio Calabria, Italy
Interests: indoor positioning; smart sensors; ultrasonic sensors; energy harvesting
Special Issues, Collections and Topics in MDPI journals

Dr. Massimo Merenda

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Guest Editor

Center of Digital Safety & Security, AIT Austrian Institute of Technology GmbH, 1210 Vienna, Austria
Interests: Internet of Things; silicon sensors; integrated sensors; RFID; energy harvesting; embedded systems; edge machine learning
Special Issues, Collections and Topics in MDPI journals

Dr. Demetrio Iero

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Guest Editor

Department of Information, Infrastructures and Sustainable Energy, Mediterranea University of Reggio Calabria, 89122 Reggio Calabria, Italy
Interests: indoor positioning; smart sensors; energy harvesting; solar systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

There is an increasing interest about indoor positioning, which is an emerging technology with a wide range of applications. Accurate and real-time positioning enables augmented and mixed reality applications, human–machine and home automation gestural interfaces, and navigation in shopping centers. Relevant applications include robotics, acquiring the position of flexible arms, navigation of unmanned automatic vehicles, security, virtual fencing of sensitive locations, safety, and preventing accidents through the recognition of dangerous postures and positions in workers. Further fields of application include medicine, such as monitoring elderly people’s movements or rehabilitative exercises; logistics, such as the positioning of goods in warehouses; sport, such as monitoring body and limb position during training exercises and in game consoles.

At present, research effort needs to be directed to new algorithms, architectures, sensor technologies, coverage, power consumption, size, and increased spatial and temporal resolution of indoor positioning systems, based on the physical and economic constraints of the various applications. In this framework, we are glad to edit this Special Issue on “Sensors and Systems for Indoor Positioning". Original contributions focused on systems and technologies to enable the indoor applications listed above are welcome.

Prof. Dr. Riccardo Carotenuto
Dr. Massimo Merenda
Dr. Demetrio Iero
Guest Editors

Manuscript Submission Information

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Keywords

indoor positioning
positioning strategies
position sensors
acoustic emitters and sensors for positioning
magnetic positioning sensors
Bluetooth and WiFi positioning sensors
positioning systems and infrastructures
positioning algorithms
active and passive positioning
sensorless positioning
positioning deep learning

Published Papers (12 papers)

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Editorial

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5 pages, 171 KiB

Open AccessEditorial

Advanced Sensors and Systems Technologies for Indoor Positioning

by Riccardo Carotenuto, Demetrio Iero and Massimo Merenda

Sensors 2022, 22(10), 3605; https://doi.org/10.3390/s22103605 - 10 May 2022

Viewed by 1325

Abstract

There is an increasing interest about indoor positioning, which is an emerging technology with a wide range of applications [...] Full article

(This article belongs to the Special Issue Sensors and Systems for Indoor Positioning)

Research

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22 pages, 3137 KiB

Open AccessArticle

Indoor Carrier Phase Positioning Technology Based on OFDM System

by Zhenyu Zhang, Shaoli Kang and Xiang Zhang

Sensors 2021, 21(20), 6731; https://doi.org/10.3390/s21206731 - 11 Oct 2021

Cited by 4 | Viewed by 2112

Abstract

Carrier phase measurement is a ranging technique that uses the receiver to determine the phase difference between the received signal and the transmitted signal. Carrier phase ranging has a high resolution; thus, it is an important research direction for high precision positioning. It is widely used in global navigation satellite systems (GNSS) systems but is not yet commonly used inwireless orthogonal frequency division multiplex (OFDM) systems. Applying carrier phase technology to OFDM systems can significantly improve positioning accuracy. Like GNSS carrier phase positioning, using the OFDM carrier phase for positioning has the following two problems. First, multipath and non-line-of-sight (NLOS) propagation have severe effects on carrier phase measurements. Secondly, ambiguity resolution is also a primary issue in the carrier phase positioning. This paper presents a ranging scheme based on the carrier phase in a multipath environment. Moreover, an algorithm based on the extended Kalman filter (EKF) is developed for fast integer ambiguity resolution and NLOS error mitigation. The simulation results show that the EKF algorithm proposed in this paper solves the integer ambiguity quickly. Further, the high-resolution carrier phase measurements combined with the accurately estimated integer ambiguity lead to less than 30-centimeter positioning error for 90% of the terminals. In conclusion, the presented methods gain excellent performance, even when NLOS error occur. Full article

(This article belongs to the Special Issue Sensors and Systems for Indoor Positioning)

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20 pages, 7338 KiB

Open AccessArticle

An Action Classification Method for Forklift Monitoring in Industry 4.0 Scenarios

by Andrea Motroni, Alice Buffi, Paolo Nepa, Mario Pesi and Antonio Congi

Sensors 2021, 21(15), 5183; https://doi.org/10.3390/s21155183 - 30 Jul 2021

Cited by 19 | Viewed by 2800

Abstract

The I-READ 4.0 project is aimed at developing an integrated and autonomous Cyber-Physical System for automatic management of very large warehouses with a high-stock rotation index. Thanks to a network of Radio Frequency Identification (RFID) readers operating in the Ultra-High-Frequency (UHF) band, both fixed and mobile, it is possible to implement an efficient management of assets and forklifts operating in an indoor scenario. A key component to accomplish this goal is the UHF-RFID Smart Gate, which consists of a checkpoint infrastructure based on RFID technology to identify forklifts and their direction of transit. This paper presents the implementation of a UHF-RFID Smart Gate with a single reader antenna with asymmetrical deployment, thus allowing the correct action classification with reduced infrastructure complexity and cost. The action classification method exploits the signal phase backscattered by RFID tags placed on the forklifts. The performance and the method capabilities are demonstrated through an on-site demonstrator in a real warehouse. Full article

(This article belongs to the Special Issue Sensors and Systems for Indoor Positioning)

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12 pages, 1091 KiB

Open AccessArticle

Ranging with Frequency Dependent Ultrasound Air Attenuation

by Riccardo Carotenuto, Fortunato Pezzimenti, Francesco G. Della Corte, Demetrio Iero and Massimo Merenda

Sensors 2021, 21(15), 4963; https://doi.org/10.3390/s21154963 - 21 Jul 2021

Cited by 3 | Viewed by 1923

Abstract

Measuring the distance between two points has multiple uses. Position can be geometrically calculated from multiple measurements of the distance between reference points and moving sensors. Distance measurement can be done by measuring the time of flight of an ultrasonic signal traveling from an emitter to receiving sensors. However, this requires close synchronization between the emitter and the sensors. This synchronization is usually done using a radio or optical channel, which requires additional hardware and power to operate. On the other hand, for many applications of great interest, low-cost, small, and lightweight sensors with very small batteries are required. Here, an innovative technique to measure the distance between emitter and receiver by using ultrasonic signals in air is proposed. In fact, the amount of the signal attenuation in air depends on the frequency content of the signal itself. The attenuation level that the signal undergoes at different frequencies provides information on the distance between emitter and receiver without the need for any synchronization between them. A mathematical relationship here proposed allows for estimating the distance between emitter and receiver starting from the measurement of the frequency dependent attenuation along the traveled path. The level of attenuation in the air is measured online along the operation of the proposed technique. The simulations showed that the range accuracy increases with the decrease of the ultrasonic transducer diameter. In particular, with a diameter of 0.5 mm, an error of less than ± 2.7 cm (average value 1.1 cm) is reached along two plane sections of the typical room of the office considered (4 × 4 × 3 m³). Full article

(This article belongs to the Special Issue Sensors and Systems for Indoor Positioning)

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22 pages, 2035 KiB

Open AccessArticle

Comparison of Direct Intersection and Sonogram Methods for Acoustic Indoor Localization of Persons

by Dominik Jan Schott, Addythia Saphala, Georg Fischer, Wenxin Xiong, Andrea Gabbrielli, Joan Bordoy, Fabian Höflinger, Kai Fischer, Christian Schindelhauer and Stefan Johann Rupitsch

Sensors 2021, 21(13), 4465; https://doi.org/10.3390/s21134465 - 29 Jun 2021

Cited by 3 | Viewed by 1925

Abstract

We discuss two methods to detect the presence and location of a person in an acoustically small-scale room and compare the performances for a simulated person in distances between 1 and 2 m. The first method is Direct Intersection, which determines a coordinate point based on the intersection of spheroids defined by observed distances of high-intensity reverberations. The second method, Sonogram analysis, overlays all channels’ room impulse responses to generate an intensity map for the observed environment. We demonstrate that the former method has lower computational complexity that almost halves the execution time in the best observed case, but about 7 times slower in the worst case compared to the Sonogram method while using 2.4 times less memory. Both approaches yield similar mean absolute localization errors between 0.3 and 0.9 m. The Direct Intersection method performs more precise in the best case, while the Sonogram method performs more robustly. Full article

(This article belongs to the Special Issue Sensors and Systems for Indoor Positioning)

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16 pages, 5207 KiB

Open AccessArticle

Clustering-Based Noise Elimination Scheme for Data Pre-Processing for Deep Learning Classifier in Fingerprint Indoor Positioning System

by Shuzhi Liu, Rashmi Sharan Sinha and Seung-Hoon Hwang

Sensors 2021, 21(13), 4349; https://doi.org/10.3390/s21134349 - 25 Jun 2021

Cited by 10 | Viewed by 2230

Abstract

Wi-Fi-based indoor positioning systems have a simple layout and a low cost, and they have gradually become popular in both academia and industry. However, due to the poor stability of Wi-Fi signals, it is difficult to accurately decide the position based on a received signal strength indicator (RSSI) by using a traditional dataset and a deep learning classifier. To overcome this difficulty, we present a clustering-based noise elimination scheme (CNES) for RSSI-based datasets. The scheme facilitates the region-based clustering of RSSIs through density-based spatial clustering of applications with noise. In this scheme, the RSSI-based dataset is preprocessed and noise samples are removed by CNES. This experiment was carried out in a dynamic environment, and we evaluated the lab simulation results of CNES using deep learning classifiers. The results showed that applying CNES to the test database to eliminate noise will increase the success probability of fingerprint location. The lab simulation results show that after using CNES, the average positioning accuracy of margin-zero (zero-meter error), margin-one (two-meter error), and margin-two (four-meter error) in the database increased by 17.78%, 7.24%, and 4.75%, respectively. We evaluated the simulation results with a real time testing experiment, where the result showed that CNES improved the average positioning accuracy to 22.43%, 9.15%, and 5.21% for margin-zero, margin-one, and margin-two error, respectively. Full article

(This article belongs to the Special Issue Sensors and Systems for Indoor Positioning)

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37 pages, 10268 KiB

Open AccessArticle

Development of a Smartphone-Based University Library Navigation and Information Service Employing Wi-Fi Location Fingerprinting

by Guenther Retscher and Alexander Leb

Sensors 2021, 21(2), 432; https://doi.org/10.3390/s21020432 - 09 Jan 2021

Cited by 15 | Viewed by 3061

Abstract

A guidance and information service for a University library based on Wi-Fi signals using fingerprinting as chosen localization method is under development at TU Wien. After a thorough survey of suitable location technologies for the application it was decided to employ mainly Wi-Fi for localization. For that purpose, the availability, performance, and usability of Wi-Fi in selected areas of the library are analyzed in a first step. These tasks include the measurement of Wi-Fi received signal strengths (RSS) of the visible access points (APs) in different areas. The measurements were carried out in different modes, such as static, kinematic and in stop-and-go mode, with six different smartphones. A dependence on the positioning and tracking modes is seen in the tests. Kinematic measurements pose much greater challenges and depend significantly on the duration of a single Wi-Fi scan. For the smartphones, the scan durations differed in the range of 2.4 to 4.1 s resulting in different accuracies for kinematic positioning, as fewer measurements along the trajectories are available for a device with longer scan duration. The investigations indicated also that the achievable localization performance is only on the few meter level due to the small number of APs of the University own Wi-Fi network deployed in the library. A promising solution for performance improvement is the foreseen usage of low-cost Raspberry Pi units serving as Wi-Fi transmitter and receiver. Full article

(This article belongs to the Special Issue Sensors and Systems for Indoor Positioning)

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14 pages, 6288 KiB

Open AccessArticle

Demonstration of Three-Dimensional Indoor Visible Light Positioning with Multiple Photodiodes and Reinforcement Learning

by Zhuo Zhang, Huayang Chen, Weikang Zeng, Xinlong Cao, Xuezhi Hong and Jiajia Chen

Sensors 2020, 20(22), 6470; https://doi.org/10.3390/s20226470 - 12 Nov 2020

Cited by 4 | Viewed by 1755

Abstract

To provide high-quality location-based services in the era of the Internet of Things, visible light positioning (VLP) is considered a promising technology for indoor positioning. In this paper, we study a multi-photodiodes (multi-PDs) three-dimensional (3D) indoor VLP system enhanced by reinforcement learning (RL), which can realize accurate positioning in the 3D space without any off-line training. The basic 3D positioning model is introduced, where without height information of the receiver, the initial height value is first estimated by exploring its relationship with the received signal strength (RSS), and then, the coordinates of the other two dimensions (i.e., X and Y in the horizontal plane) are calculated via trilateration based on the RSS. Two different RL processes, namely RL₁ and RL₂, are devised to form two methods that further improve horizontal and vertical positioning accuracy, respectively. A combination of RL₁ and RL₂ as the third proposed method enhances the overall 3D positioning accuracy. The positioning performance of the four presented 3D positioning methods, including the basic model without RL (i.e., Benchmark) and three RL based methods that run on top of the basic model, is evaluated experimentally. Experimental results verify that obviously higher 3D positioning accuracy is achieved by implementing any proposed RL based methods compared with the benchmark. The best performance is obtained when using the third RL based method that runs RL₂ and RL₁ sequentially. For the testbed that emulates a typical office environment with a height difference between the receiver and the transmitter ranging from 140 cm to 200 cm, an average 3D positioning error of 2.6 cm is reached by the best RL method, demonstrating at least 20% improvement compared to the basic model without performing RL. Full article

(This article belongs to the Special Issue Sensors and Systems for Indoor Positioning)

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32 pages, 8737 KiB

Open AccessArticle

Indoor Localization Based on Infrared Angle of Arrival Sensor Network

by Damir Arbula and Sandi Ljubic

Sensors 2020, 20(21), 6278; https://doi.org/10.3390/s20216278 - 04 Nov 2020

Cited by 20 | Viewed by 4339

Abstract

Accurate, inexpensive, and reliable real-time indoor localization holds the key to the full potential of the context-aware applications and location-based Internet of Things (IoT) services. State-of-the-art indoor localization systems are coping with the complex non-line-of-sight (NLOS) signal propagation which hinders the use of proven multiangulation and multilateration methods, as well as with prohibitive installation costs, computational demands, and energy requirements. In this paper, we present a novel sensor utilizing low-range infrared (IR) signal in the line-of-sight (LOS) context providing high precision angle-of-arrival (AoA) estimation. The proposed sensor is used in the pragmatic solution to the localization problem that avoids NLOS propagation issues by exploiting the powerful concept of the wireless sensor network (WSN). To demonstrate the proposed solution, we applied it in the challenging context of the supermarket cart navigation. In this specific use case, a proof-of-concept navigation system was implemented with the following components: IR-AoA sensor prototype and the corresponding WSN used for cart localization, server-side application programming interface (API), and client application suite consisting of smartphone and smartwatch applications. The localization performance of the proposed solution was assessed in, altogether, four evaluation procedures, including both empirical and simulation settings. The evaluation outcomes are ranging from centimeter-level accuracy achieved in static-1D context up to 1 m mean localization error obtained for a mobile cart moving at 140 cm/s in a 2D setup. These results show that, for the supermarket context, appropriate localization accuracy can be achieved, along with the real-time navigation support, using readily available IR technology with inexpensive hardware components. Full article

(This article belongs to the Special Issue Sensors and Systems for Indoor Positioning)

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24 pages, 4803 KiB

Open AccessArticle

Adaptive Residual Weighted K-Nearest Neighbor Fingerprint Positioning Algorithm Based on Visible Light Communication

by Shiwu Xu, Chih-Cheng Chen, Yi Wu, Xufang Wang and Fen Wei

Sensors 2020, 20(16), 4432; https://doi.org/10.3390/s20164432 - 08 Aug 2020

Cited by 22 | Viewed by 2416

Abstract

The weighted K-nearest neighbor (WKNN) algorithm is a commonly used fingerprint positioning, the difficulty of which lies in how to optimize the value of K to obtain the minimum positioning error. In this paper, we propose an adaptive residual weighted K-nearest neighbor (ARWKNN) fingerprint positioning algorithm based on visible light communication. Firstly, the target matches the fingerprints according to the received signal strength indication (RSSI) vector. Secondly, K is a dynamic value according to the matched RSSI residual. Simulation results show the ARWKNN algorithm presents a reduced average positioning error when compared with random forest (81.82%), extreme learning machine (83.93%), artificial neural network (86.06%), grid-independent least square (60.15%), self-adaptive WKNN (43.84%), WKNN (47.81%), and KNN (73.36%). These results were obtained when the signal-to-noise ratio was set to 20 dB, and Manhattan distance was used in a two-dimensional (2-D) space. The ARWKNN algorithm based on Clark distance and minimum maximum distance metrics produces the minimum average positioning error in 2-D and 3-D, respectively. Compared with self-adaptive WKNN (SAWKNN), WKNN and KNN algorithms, the ARWKNN algorithm achieves a significant reduction in the average positioning error while maintaining similar algorithm complexity. Full article

(This article belongs to the Special Issue Sensors and Systems for Indoor Positioning)

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14 pages, 3188 KiB

Open AccessArticle

Simulating Signal Aberration and Ranging Error for Ultrasonic Indoor Positioning

by Riccardo Carotenuto, Massimo Merenda, Demetrio Iero and Francesco G. Della Corte

Sensors 2020, 20(12), 3548; https://doi.org/10.3390/s20123548 - 23 Jun 2020

Cited by 16 | Viewed by 2560

Abstract

Increasing efforts toward the development of positioning techniques testify the growing interest for indoor position-based applications and services. Many applications require accurate indoor positioning or tracking of people and assets, and some market sectors are starting a rapid growth of products based on these technologies. Ultrasonic systems have already been demonstrating their effectiveness and to possess the desired positioning accuracy and refresh rates. In this work, it is shown that a typical signal used in ultrasonic positioning systems to estimate the range between the target and reference points—namely, the linear chirp—due to the effects of acoustic diffraction, in some cases, undergoes a shape aberration, depending on the shape and size of the transducer and on the angle under which the transducer is seen by the receiver. In the presence of such signal shape aberrations, even one of the most robust ranging techniques, which is based on cross-correlation, provides results affected by a much greater error than expected. Numerical simulations are carried out for a typical ultrasonic chirp, ultrasonic emitter, and range technique based on cross-correlation and for a typical office room, obtained using the academic acoustic simulation software Field II. Spatial distributions of the ranging error are provided, clearly showing the favorable low error regions. The work demonstrates that particular attention must be paid to the design of the acoustic section of the ultrasonic positioning systems, considering both the shape and size of the ultrasonic emitters and the shape of the acoustic signal used. Full article

(This article belongs to the Special Issue Sensors and Systems for Indoor Positioning)

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13 pages, 3534 KiB

Open AccessLetter

A Recursive Algorithm for Indoor Positioning Using Pulse-Echo Ultrasonic Signals

by Salvatore A. Pullano, Maria Giovanna Bianco, Davide C. Critello, Michele Menniti, Antonio La Gatta and Antonino S. Fiorillo

Sensors 2020, 20(18), 5042; https://doi.org/10.3390/s20185042 - 04 Sep 2020

Cited by 13 | Viewed by 2465

Abstract

Low frequency ultrasounds in air are widely used for real-time applications in short-range communication systems and environmental monitoring, in both structured and unstructured environments. One of the parameters widely evaluated in pulse-echo ultrasonic measurements is the time of flight (TOF), which can be evaluated with an increased accuracy and complexity by using different techniques. Hereafter, a nonstandard cross-correlation method is investigated for TOF estimations. The procedure, based on the use of template signals, was implemented to improve the accuracy of recursive TOF evaluations. Tests have been carried out through a couple of 60 kHz custom-designed polyvinylidene fluoride (PVDF) hemicylindrical ultrasonic transducers. The experimental results were then compared with the standard threshold and cross-correlation techniques for method validation and characterization. An average improvement of 30% and 19%, in terms of standard error (SE), was observed. Moreover, the experimental results evidenced an enhancement in repeatability of about 10% in the use of a recursive positioning system. Full article

(This article belongs to the Special Issue Sensors and Systems for Indoor Positioning)

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