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Sensors and Systems for Indoor Positioning

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A topical collection in Sensors (ISSN 1424-8220). This collection belongs to the section "Navigation and Positioning".

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Editors

Prof. Dr. Riccardo Carotenuto

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Collection 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
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Dr. Massimo Merenda

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Collection Editor

DIIES Department, Via dell'Università, Reggio Calabria, RC, Italy
Interests: Internet of Things; silicon sensors; integrated sensors; RFID; energy harvesting; embedded systems; edge machine learning
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Dr. Demetrio Iero

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Collection 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
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Topical Collection Information

Dear Colleagues,

There is an increasing interest in 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; and sport, such as monitoring body and limb position during training exercises and in game consoles.

At present, research efforts need 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, it is our pleasure to edit this Collection 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
Collection Editors

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Keywords

indoor positioning
positioning strategies
position sensors
acoustic emitters and sensors for positioning
magnetic positioning sensors
bluetooth and Wi-Fi positioning sensors
positioning systems and infrastructures
positioning algorithms
active and passive positioning
sensorless positioning
positioning deep learning

Published Papers (9 papers)

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2024

Jump to: 2023

21 pages, 6251 KB

Open AccessArticle

A High-Resolution Time Reversal Method for Target Localization in Reverberant Environments

by Huiying Ma, Tao Shang, Gufeng Li and Zhaokun Li

Sensors 2024, 24(10), 3196; https://doi.org/10.3390/s24103196 - 17 May 2024

Cited by 1 | Viewed by 1726

Abstract

Reverberation in real environments is an important factor affecting the high resolution of target sound source localization (SSL) methods. Broadband low-frequency signals are common in real environments. This study focuses on the localization of this type of signal in reverberant environments. Because the time reversal (TR) method can overcome multipath effects and realize adaptive focusing, it is particularly suitable for SSL in a reverberant environment. On the basis of the significant advantages of the sparse Bayesian learning algorithm in the estimation of wave direction, a novel SSL is proposed in reverberant environments. First, the sound propagation model in a reverberant environment is studied and the TR focusing signal is obtained. We then use the sparse Bayesian framework to locate the broadband low-frequency sound source. To validate the effectiveness of the proposed method for broadband low-frequency targeting in a reverberant environment, simulations and real data experiments were performed. The localization performance under different bandwidths, different numbers of microphones, signal-to-noise ratios, reverberation times, and off-grid conditions was studied in the simulation experiments. The practical experiment was conducted in a reverberation chamber. Simulation and experimental results indicate that the proposed method can achieve satisfactory spatial resolution in reverberant environments and is robust. Full article

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33 pages, 3053 KB

Open AccessArticle

A Performance Comparison between Different Industrial Real-Time Indoor Localization Systems for Mobile Platforms

by Paulo M. Rebelo, José Lima, Salviano Pinto Soares, Paulo Moura Oliveira, Héber Sobreira and Pedro Costa

Sensors 2024, 24(7), 2095; https://doi.org/10.3390/s24072095 - 25 Mar 2024

Cited by 2 | Viewed by 2659

Abstract

The flexibility and versatility associated with autonomous mobile robots (AMR) have facilitated their integration into different types of industries and tasks. However, as the main objective of their implementation on the factory floor is to optimize processes and, consequently, the time associated with them, it is necessary to take into account the environment and congestion to which they are subjected. Localization, on the shop floor and in real time, is an important requirement to optimize the AMRs’ trajectory management, thus avoiding livelocks and deadlocks during their movements in partnership with manual forklift operators and logistic trains. Threeof the most commonly used localization techniques in indoor environments (time of flight, angle of arrival, and time difference of arrival), as well as two of the most commonly used indoor localization methods in the industry (ultra-wideband, and ultrasound), are presented and compared in this paper. Furthermore, it identifies and compares three industrial indoor localization solutions: Qorvo, Eliko Kio, and Marvelmind, implemented in an industrial mobile platform, which is the main contribution of this paper. These solutions can be applied to both AMRs and other mobile platforms, such as forklifts and logistic trains. In terms of results, the Marvelmind system, which uses an ultrasound method, was the best solution. Full article

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19 pages, 607 KB

Open AccessEditor’s ChoiceArticle

Simplified Indoor Localization Using Bluetooth Beacons and Received Signal Strength Fingerprinting with Smartwatch

by Leana Bouse, Scott A. King and Tianxing Chu

Sensors 2024, 24(7), 2088; https://doi.org/10.3390/s24072088 - 25 Mar 2024

Cited by 13 | Viewed by 5173

Abstract

Variations in Global Positioning Systems (GPSs) have been used for tracking users’ locations. However, when location tracking is needed for an indoor space, such as a house or building, then an alternative means of precise position tracking may be required because GPS signals can be severely attenuated or completely blocked. In our approach to indoor positioning, we developed an indoor localization system that minimizes the amount of effort and cost needed by the end user to put the system to use. This indoor localization system detects the user’s room-level location within a house or indoor space in which the system has been installed. We combine the use of Bluetooth Low Energy beacons and a smartwatch Bluetooth scanner to determine which room the user is located in. Our system has been developed specifically to create a low-complexity localization system using the Nearest Neighbor algorithm and a moving average filter to improve results. We evaluated our system across a household under two different operating conditions: first, using three rooms in the house, and then using five rooms. The system was able to achieve an overall accuracy of 85.9% when testing in three rooms and 92.106% across five rooms. Accuracy also varied by region, with most of the regions performing above 96% accuracy, and most false-positive incidents occurring within transitory areas between regions. By reducing the amount of processing used by our approach, the end-user is able to use other applications and services on the smartwatch concurrently. Full article

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16 pages, 6332 KB

Open AccessArticle

A Method for Correcting Signal Aberrations in Ultrasonic Indoor Positioning

by Riccardo Carotenuto, Demetrio Iero and Massimo Merenda

Sensors 2024, 24(6), 2017; https://doi.org/10.3390/s24062017 - 21 Mar 2024

Cited by 3 | Viewed by 1656

Abstract

The increasing focus on the development of positioning techniques reflects the growing interest in applications and services based on indoor positioning. Many applications necessitate precise indoor positioning or tracking of individuals and assets, leading to rapid growth in products based on these technologies in certain market sectors. Ultrasonic systems have already proven effective in achieving the desired positioning accuracy and refresh rates. The typical signal used in ultrasonic positioning systems for estimating the range between the target and reference points is the linear chirp. Unfortunately, it can undergo shape aberration due to the effects of acoustic diffraction when the aperture exceeds a certain limit. The extent of the aberration is influenced by the shape and size of the transducer, as well as the angle at which the transducer is observed by the receiver. This aberration also affects the shape of the cross-correlation, causing it to lose its easily detectable characteristic of a single global peak, which typically corresponds to the correct lag associated with the signal’s time of arrival. In such instances, cross-correlation techniques yield results with a significantly higher error than anticipated. In fact, the correct lag no longer corresponds to the peak of the cross-correlation. In this study, an alternative technique to global peak detection is proposed, leveraging the inherent symmetry observed in the shape of the aberrated cross-correlation. The numerical simulations, performed using the academic acoustic simulation software Field II, conducted using a typical ultrasonic chirp and ultrasonic emitter, compare the classical and the proposed range techniques in a standard office room. The analysis includes the effects of acoustical reflection in the room and of the acoustic noise at different levels of power. The results demonstrate that the proposed technique enables accurate range estimation even in the presence of severe cross-correlation shape aberrations and for signal-to-noise ratio levels common in office and room environments, even in presence of typical reflections. This allows the use of emitting transducers with a much larger aperture than that allowed by the classical cross-correlation technique. Consequently, it becomes possible to have greater acoustic power available, leading to improved signal-to-noise ratio (SNR). Full article

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19 pages, 882 KB

Open AccessArticle

Multi-Dimensional Wi-Fi Received Signal Strength Indicator Data Augmentation Based on Multi-Output Gaussian Process for Large-Scale Indoor Localization

by Zhe Tang, Sihao Li, Kyeong Soo Kim and Jeremy S. Smith

Sensors 2024, 24(3), 1026; https://doi.org/10.3390/s24031026 - 5 Feb 2024

Cited by 8 | Viewed by 3124

Abstract

Location fingerprinting using Received Signal Strength Indicators (RSSIs) has become a popular technique for indoor localization due to its use of existing Wi-Fi infrastructure and Wi-Fi-enabled devices. Artificial intelligence/machine learning techniques such as Deep Neural Networks (DNNs) have been adopted to make location fingerprinting more accurate and reliable for large-scale indoor localization applications. However, the success of DNNs for indoor localization depends on the availability of a large amount of pre-processed and labeled data for training, the collection of which could be time-consuming in large-scale indoor environments and even challenging during a pandemic situation like COVID-19. To address these issues in data collection, we investigate multi-dimensional RSSI data augmentation based on the Multi-Output Gaussian Process (MOGP), which, unlike the Single-Output Gaussian Process (SOGP), can exploit the correlation among the RSSIs from multiple access points in a single floor, neighboring floors, or a single building by collectively processing them. The feasibility of MOGP-based multi-dimensional RSSI data augmentation is demonstrated through experiments using the hierarchical indoor localization model based on a Recurrent Neural Network (RNN)—i.e., one of the state-of-the-art multi-building and multi-floor localization models—and the publicly available UJIIndoorLoc multi-building and multi-floor indoor localization database. The RNN model trained with the UJIIndoorLoc database augmented with the augmentation mode of “by a single building”, where an MOGP model is fitted based on the entire RSSI data of a building, outperforms the other two augmentation modes and results in the three-dimensional localization error of

8.42

m

. Full article

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2023

Jump to: 2024

20 pages, 8473 KB

Open AccessArticle

Research on Positioning Accuracy of Mobile Robot in Indoor Environment Based on Improved RTABMAP Algorithm

by Shijie Zhou, Zelun Li, Zhongliang Lv, Chuande Zhou, Pengcheng Wu, Changshuang Zhu and Wei Liu

Sensors 2023, 23(23), 9468; https://doi.org/10.3390/s23239468 - 28 Nov 2023

Cited by 4 | Viewed by 2378

Abstract

Visual simultaneous localization and mapping is a widely used technology for mobile robots to carry out precise positioning in the environment of GNSS technology failure. However, as the robot moves around indoors, its position accuracy will gradually decrease over time due to common and unavoidable environmental factors. In this paper, we propose an improved method called RTABMAP-VIWO, which is based on RTABMAP. The basic idea is to use an Extended Kalman Filter (EKF) framework for fusion attitude estimates from the wheel odometry and IMU, and provide new prediction values. This helps to reduce the local cumulative error of RTABMAP and make it more accurate. We compare and evaluate three kinds of SLAM methods using both public datasets and real indoor scenes. In the dataset experiments, our proposed method reduces the Root-Mean-Square Error (RMSE) coefficient by 48.1% compared to the RTABMAP, and the coefficient is also reduced by at least 29.4% in the real environment experiments. The results demonstrate that the improved method is feasible. By incorporating the IMU into the RTABMAP method, the trajectory and posture errors of the mobile robot are significantly improved. Full article

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22 pages, 1511 KB

Open AccessArticle

Efficient Localization Method Based on RSSI for AP Clusters

by Zhigang Su, Zeyu Tian and Jingtang Hao

Sensors 2023, 23(17), 7599; https://doi.org/10.3390/s23177599 - 1 Sep 2023

Cited by 1 | Viewed by 1852

Abstract

The localization accuracy is susceptible to the received signal strength indication (RSSI) fluctuations for RSSI-based wireless localization methods. Moreover, the maximum likelihood estimation (MLE) of the target location is nonconvex, and locating target presents a significant computational complexity. In this paper, an RSSI-based access point cluster localization (APCL) method is proposed for locating a moving target. Multiple location-constrained access points (APs) are used in the APCL method to form an AP cluster as an anchor node (AN) in the wireless sensor network (WSN), and the RSSI of the target is estimated with several RSSI samples obtained by the AN. With the estimated RSSI for each AN, the solution for the target location can be obtained quickly and accurately due to the fact that the MLE localization problem is transformed into an eigenvalue problem by constructing an eigenvalue equation. Simulation and experimental results show that the APCL method can meet the requirement of high-precision real-time localization of moving targets in WSN with higher localization accuracy and lower computational effort compared to the existing classical RSSI-based localization methods. Full article

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19 pages, 7814 KB

Open AccessArticle

A Novel Optimized iBeacon Localization Algorithm Modeling

by Zhengyu Yu, Liu Chu and Jiajia Shi

Sensors 2023, 23(14), 6560; https://doi.org/10.3390/s23146560 - 20 Jul 2023

Cited by 2 | Viewed by 2285

Abstract

The conventional methods for indoor localization rely on technologies such as RADAR, ultrasonic, laser range localization, beacon technology, and others. Developers in the industry have started utilizing these localization techniques in iBeacon systems that use Bluetooth sensors to measure the object’s location. The iBeacon-based system is appealing due to its low cost, ease of setup, signaling, and maintenance; however, with current technology, it is challenging to achieve high accuracy in indoor object localization or tracking. Furthermore, iBeacons’ accuracy is unsatisfactory, and they are vulnerable to other radio signal interference and environmental noise. In order to address those challenges, our study focuses on the development of error modeling algorithms for signal calibration, uncertainty reduction, and interfered noise elimination. The new error modeling is developed on the Curve Fitted Kalman Filter (CFKF) algorithms. The reliability, accuracy, and feasibility of the CFKF algorithms are tested in the experiments. The results significantly show the improvement of the accuracy and precision with this novel approach for iBeacon localization. Full article

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20 pages, 1634 KB

Open AccessArticle

Some Design Considerations in Passive Indoor Positioning Systems

by Jimmy Engström, Åse Jevinger, Carl Magnus Olsson and Jan A. Persson

Sensors 2023, 23(12), 5684; https://doi.org/10.3390/s23125684 - 18 Jun 2023

Viewed by 2004

Abstract

User location is becoming an increasingly common and important feature for a wide range of services. Smartphone owners increasingly use location-based services, as service providers add context-enhanced functionality such as car-driving routes, COVID-19 tracking, crowdedness indicators, and suggestions for nearby points of interest. However, positioning a user indoors is still problematic due to the fading of the radio signal caused by multipath and shadowing, where both have complex dependencies on the indoor environment. Location fingerprinting is a common positioning method where Radio Signal Strength (RSS) measurements are compared to a reference database of previously stored RSS values. Due to the size of the reference databases, these are often stored in the cloud. However, server-side positioning computations make preserving the user’s privacy problematic. Given the assumption that a user does not want to communicate his/her location, we pose the question of whether a passive system with client-side computations can substitute fingerprinting-based systems, which commonly use active communication with a server. We compared two passive indoor location systems based on multilateration and sensor fusion using an Unscented Kalman Filter (UKF) with fingerprinting and show how these may provide accurate indoor positioning without compromising the user’s privacy in a busy office environment. Full article

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Published Papers (9 papers)

2024

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2023

Jump to: 2024

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