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Sensors and Techniques for Indoor Positioning and Localization

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

Deadline for manuscript submissions: 20 August 2024 | Viewed by 8628

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Special Issue Editor

Dr. Antonio Moschitta

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Guest Editor

Department of Engineering, University of Perugia, 06125 Perugia, Italy
Interests: indoor and short-range positioning; statistical signal processing; battery characterization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Accurate indoor positioning is an interesting topic whose applications have impacts on various fields, that, depending on the targeted accuracy, can include line traceability, telemanipulation, telemedicine, and drone control. By overcoming the limitations of outdoor GNSS, indoor positioning techniques permit seamless indoor/outdoor positioning, and can be a strong enabler for IoT and Industry 4.0 applications. As such, various approaches and methods have been proposed in the literature, with no definite solution being competitive for most conceivable scenarios. At the measurement level, various quantities can be measured, including inertial readings, ultrasound waves, static or AC magnetic fields, radiofrequency EM waves, and image or video recordings. These measurements can be combined using various approaches, such as fingerprinting, sensor fusion between multiple measurements, and, more recently, artificial intelligence. Additional degrees of freedom can be exploited at the system design level, where a specific infrastructure can be deployed, based either on specific proprietary technology or on off-the-shelf devices, such as ultrawide-band (UWB) transceivers. Depending on the targeted accuracy and budget, additional tradeoffs can be realized by using pre-existing RF infrastructures, such as WiFi or Blueooth networks. To this end, new opportunities are offered by modern consumer devices such as tablets and smartphones, which typically have embedded video cameras, multiple sensors, wireless networking capabilities, and powerful processors. Hence, this Special Issue welcomes innovative contributions, advancing the state of the art in indoor or short-range positioning with respect to sensors, measurements, estimation techniques, systems’ architectures, devices, and applications.

Dr. Antonio Moschitta
Guest Editor

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Keywords

indoor positioning
tracking
sensors
smart sensors
algorithms
measurement
accuracy
estimation
artificial intelligence
Internet of Things

Published Papers (7 papers)

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Research

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19 pages, 480 KiB

Open AccessArticle

An Accurate Anchor-Free Contextual Received Signal Strength Approach Localization in a Wireless Sensor Network

by Nour Zaarour, Nadir Hakem and Nahi Kandil

Sensors 2024, 24(4), 1210; https://doi.org/10.3390/s24041210 - 14 Feb 2024

Viewed by 508

Abstract

Sensor localization remains a crucial function within the context of wireless sensor networks (WSNs) and is a delicate concern that has attracted many researchers’ attention. Undoubtedly, a good distance estimation between different wireless sensors allows us to estimate their accurate locations in the network well. In this article, we present a simple but very effective anchor-free localization scheme for wireless sensor networks called the contextual received signal strength approach (CRSSA) localization scheme. We use the received signal strength (RSS) values and the contextual network connectivity within an anchor-free WSN. We present and thoroughly analyze a novel joint estimation methodology for determining the range, path loss exponent (PLE), and inter-node distances in a composite fading model that addresses small-scale multipath fading and large-scale path loss shadowing effects. We formulate analytical expressions for key parameters, the node’s communication range and the PLE value, as functions of the sensor’s number, the network’s connectivity, and the network density. Once these parameters are estimated, we estimate the inter-node distances and the positions of nodes, with relatively high accuracy, based on the assumed propagation model in a two-dimensional anchor-free WSN. The effectiveness of the CRSSA is evaluated through extensive simulations assuring its estimation accuracy in anchor-free localization. Full article

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

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16 pages, 18639 KiB

Open AccessArticle

Wand-Based Calibration of Unsynchronized Multiple Cameras for 3D Localization

by Sujie Zhang and Qiang Fu

Sensors 2024, 24(1), 284; https://doi.org/10.3390/s24010284 - 03 Jan 2024

Viewed by 696

Abstract

Three-dimensional (3D) localization plays an important role in visual sensor networks. However, the frame rate and flexibility of the existing vision-based localization systems are limited by using synchronized multiple cameras. For such a purpose, this paper focuses on developing an indoor 3D localization system based on unsynchronized multiple cameras. First of all, we propose a calibration method for unsynchronized perspective/fish-eye cameras based on timestamp matching and pixel fitting by using a wand under general motions. With the multi-camera calibration result, we then designed a localization method for the unsynchronized multi-camera system based on the extended Kalman filter (EKF). Finally, extensive experiments were conducted to demonstrate the effectiveness of the established 3D localization system. The obtained results provided valuable insights into the camera calibration and 3D localization of unsynchronized multiple cameras in visual sensor networks. Full article

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

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24 pages, 4996 KiB

Open AccessArticle

Design of Acoustic Signal for Positioning of Smart Devices

by Veronika Hromadova, Peter Brida and Juraj Machaj

Sensors 2023, 23(18), 7852; https://doi.org/10.3390/s23187852 - 13 Sep 2023

Viewed by 911

Abstract

This paper addresses the limitations of using smartphones in innovative localization systems based on audio signal processing, particularly in the frequency range of 18–22 kHz, due to the lack of technical specifications and noise characterization. We present a comprehensive study on signal design and performance analysis for acoustic communication in air ducts, focusing on signal propagation in indoor environments considering room acoustics and signal behavior. The research aims to determine optimal parameters, including the frequency band, signal types, signal length, pause duration, and sampling frequency, for the efficient transmission and reception of acoustic signals for commercial off-the-shelf (COST) devices. Factors like inter-symbol interference (ISI) and multiple access interference (MAI) that affect signal detection accuracy are considered. The measurements help define the frequency spectrum for common devices like smartphones, speakers, and sound cards. We propose a custom signal with specific properties and reasons for their selection, setting the signal length at 50 ms and a pause time of 5 ms to minimize overlap and interference between consecutive signals. The sampling rate is fixed at 48 kHz to maintain the required resolution for distinguishing individual signals in correlation-based signal processing. Full article

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

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32 pages, 6791 KiB

Open AccessArticle

A Hybrid Indoor Positioning System Based on Visible Light Communication and Bluetooth RSS Trilateration

by Lamya Albraheem and Sarah Alawad

Sensors 2023, 23(16), 7199; https://doi.org/10.3390/s23167199 - 16 Aug 2023

Cited by 4 | Viewed by 1273

Abstract

Indoor positioning has become an attractive research topic because of the drawbacks of the global navigation satellite system (GNSS), which cannot detect accurate locations within indoor areas. Radio-based positioning technologies are one major category of indoor positioning systems. Another major category consists of visible light communication-based solutions, as they have become a revolutionary technology for indoor positioning in recent years. The proposed study intends to make use of both technologies by creating a hybrid indoor positioning system that uses VLC and Bluetooth together. The system first collects the initial location information based on VLC proximity, then collects the strongest Bluetooth signals to determine the receiver’s location using Bluetooth RSS (received signal strength) trilateration. This has been inspired by the fact that there have not been any studies that make use of both technologies with the same positioning algorithm, which can lead to pretty high accuracy of up to 0.03 m. Full article

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

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19 pages, 2623 KiB

Open AccessArticle

Indoor Visible Light Positioning System Based on Point Classification Using Artificial Intelligence Algorithms

by Qianqian Long, Junyi Zhang, Lu Cao and Wenrui Wang

Sensors 2023, 23(11), 5224; https://doi.org/10.3390/s23115224 - 31 May 2023

Cited by 2 | Viewed by 1185

Abstract

In RSSI-based indoor visible light positioning systems, when only RSSI is used for trilateral positioning, the receiver height needs to be known to calculate distance. Meanwhile, the positioning accuracy is greatly affected by multi-path effect interference, with the influence of the multi-path effect varying across different areas of the room. If only one single processing is used for positioning, the positioning error in the edge area will increase sharply. In order to address these problems, this paper proposes a new positioning scheme, which uses artificial intelligence algorithms for point classification. Firstly, height estimation is performed according to the received power data structure from different LEDs, which effectively extends the traditional RSSI trilateral positioning from 2D to 3D. The location points in the room are then divided into three categories: ordinary points, edge points and blind points, and corresponding models are used to process different types of points, respectively, to reduce the influence of the multi-path effect. Next, processed received power data are used in the trilateral positioning method for calculating the location point coordinates, and to reduce the room edge corner positioning error, so as to reduce the indoor average positioning error. Finally, a complete system is built in an experimental simulation to verify the effectiveness of the proposed schemes, which are shown to achieve centimeter-level positioning accuracy. Full article

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

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18 pages, 3221 KiB

Open AccessArticle

UWB Localization Based on Improved Robust Adaptive Cubature Kalman Filter

by Jiaqi Dong, Zengzeng Lian, Jingcheng Xu and Zhe Yue

Sensors 2023, 23(5), 2669; https://doi.org/10.3390/s23052669 - 28 Feb 2023

Cited by 8 | Viewed by 1366

Abstract

Aiming at the problems of Non-Line-of-Sight (NLOS) observation errors and inaccurate kinematic model in ultra-wideband (UWB) systems, this paper proposed an improved robust adaptive cubature Kalman filter (IRACKF). Robust and adaptive filtering can weaken the influence of observed outliers and kinematic model errors on filtering, respectively. However, their application conditions are different, and improper use may reduce positioning accuracy. Therefore, this paper designed a sliding window recognition scheme based on polynomial fitting, which can process the observation data in real-time to identify error types. Simulation and experimental results indicate that compared to the robust CKF, adaptive CKF, and robust adaptive CKF, the IRACKF algorithm reduces the position error by 38.0%, 45.1%, and 25.3%, respectively. The proposed IRACKF algorithm significantly improves the positioning accuracy and stability of the UWB system. Full article

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

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Review

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26 pages, 853 KiB

Open AccessReview

A Survey of the Performance-Limiting Factors of a 2-Dimensional RSS Fingerprinting-Based Indoor Wireless Localization System

by Abdulmalik Shehu Yaro, Filip Maly and Pavel Prazak

Sensors 2023, 23(5), 2545; https://doi.org/10.3390/s23052545 - 24 Feb 2023

Cited by 10 | Viewed by 1943

Abstract

A receive signal strength (RSS) fingerprinting-based indoor wireless localization system (I-WLS) uses a localization machine learning (ML) algorithm to estimate the location of an indoor user using RSS measurements as the position-dependent signal parameter (PDSP). There are two stages in the system’s localization process: the offline phase and the online phase. The offline phase starts with the collection and generation of RSS measurement vectors from radio frequency (RF) signals received at fixed reference locations, followed by the construction of an RSS radio map. In the online phase, the instantaneous location of an indoor user is found by searching the RSS-based radio map for a reference location whose RSS measurement vector corresponds to the user’s instantaneously acquired RSS measurements. The performance of the system depends on a number of factors that are present in both the online and offline stages of the localization process. This survey identifies these factors and examines how they impact the overall performance of the 2-dimensional (2-D) RSS fingerprinting-based I-WLS. The effects of these factors are discussed, as well as previous researchers’ suggestions for minimizing or mitigating them and future research trends in RSS fingerprinting-based I-WLS. Full article

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

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