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27 pages, 2467 KiB

Open AccessArticle

Indoor Positioning System Based on Bluetooth Low Energy Technology and a Nature-Inspired Optimization Algorithm

by Primož Bencak, Darko Hercog and Tone Lerher

Electronics 2022, 11(3), 308; https://doi.org/10.3390/electronics11030308 - 19 Jan 2022

Cited by 24 | Viewed by 5563

Warehousing is one of the most important activities in the supply chain, enabling competitive advantage. Effective management of warehousing processes is, therefore, crucial for achieving minimal costs, maximum efficiency, and overall customer satisfaction. Warehouse Management Systems (WMS) are the first steps towards organizing [...] Read more.

Warehousing is one of the most important activities in the supply chain, enabling competitive advantage. Effective management of warehousing processes is, therefore, crucial for achieving minimal costs, maximum efficiency, and overall customer satisfaction. Warehouse Management Systems (WMS) are the first steps towards organizing these processes; however, due to the human factor involved, information on products, vehicles and workers may be missing, corrupt, or misleading. In this paper, a cost-effective Indoor Positioning System (IPS) based on Bluetooth Low Energy (BLE) technology is presented for use in Intralogistics that works automatically, and therefore minimizes the possibility of acquiring incorrect data. The proposed IPS solution is intended to be used for supervising order-picker movements, movement of packages between workstations, and tracking other mobile devices in a manually operated warehouse. Only data that are accurate, reliable and represent the actual state of the system, are useful for detailed material flow analysis and optimization in Intralogistics. Using the developed solution, IPS technology is leveraged to enhance the manually operated warehouse operational efficiency in Intralogistics. Due to the hardware independence, the developed software solution can be used with virtually any BLE supported beacons and receivers. The results of IPS testing in laboratory/office settings show that up to 98% of passings are detected successfully with time delays between approach and detection of less than 0.5 s. Full article

(This article belongs to the Special Issue Indoor Positioning Techniques)

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17 pages, 4213 KiB

Open AccessFeature PaperArticle

Using Bluetooth Low Energy Technology to Perform ToF-Based Positioning

by Antonella Comuniello, Alessio De Angelis, Antonio Moschitta and Paolo Carbone

Electronics 2022, 11(1), 111; https://doi.org/10.3390/electronics11010111 - 30 Dec 2021

Cited by 6 | Viewed by 3299

Abstract

Many distributed systems that perform indoor positioning are often based on ultrasound signals and time domain measurements exchanged between low-cost ultrasound transceivers. Synchronization between transmitters and receivers is usually needed. In this paper, the use of BLE technology to achieve time synchronization by [...] Read more.

Many distributed systems that perform indoor positioning are often based on ultrasound signals and time domain measurements exchanged between low-cost ultrasound transceivers. Synchronization between transmitters and receivers is usually needed. In this paper, the use of BLE technology to achieve time synchronization by wirelessly triggered ultrasound transceivers is analyzed. Building on a previous work, the proposed solution uses BLE technology as communication infrastructure and achieves a level of synchronization compatible with Time of Flight (ToF)-based distance estimations and positioning. The proposed solution was validated experimentally. First, a measurement campaign of the time-synchronization delay for the adopted embedded platforms was carried out. Then, ToF-based distance estimations and positioning were performed. The results show that an accurate and low-cost ToF-based positioning system is achievable, using ultrasound transmissions and triggered by BLE RF transmissions. Full article

(This article belongs to the Special Issue Indoor Positioning Techniques)

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23 pages, 6845 KiB

Open AccessArticle

Evolutionary Optimization Strategy for Indoor Position Estimation Using Smartphones

by Jan Grottke and Jörg Blankenbach

Electronics 2021, 10(5), 618; https://doi.org/10.3390/electronics10050618 - 6 Mar 2021

Cited by 8 | Viewed by 2138

Abstract

Due to their distinctive presence in everyday life and the variety of available built-in sensors, smartphones have become the focus of recent indoor localization research. Hence, this paper describes a novel smartphone-based sensor fusion algorithm. It combines the relative inertial measurement unit (IMU) [...] Read more.

Due to their distinctive presence in everyday life and the variety of available built-in sensors, smartphones have become the focus of recent indoor localization research. Hence, this paper describes a novel smartphone-based sensor fusion algorithm. It combines the relative inertial measurement unit (IMU) based movements of the pedestrian dead reckoning with the absolute fingerprinting-based position estimations of Wireless Local Area Network (WLAN), Bluetooth (Bluetooth Low Energy—BLE), and magnetic field anomalies as well as a building model in real time. Thus, a step-based position estimation without knowledge of any start position was achieved. For this, a grid-based particle filter and a Bayesian filter approach were combined. Furthermore, various optimization methods were compared to weigh the different information sources within the sensor fusion algorithm, thus achieving high position accuracy. Although a particle filter was used, no particles move due to a novel grid-based particle interpretation. Here, the particles’ probability values change with every new information source and every stepwise iteration via a probability-map-based approach. By adjusting the weights of the individual measurement methods compared to a knowledge-based reference, the mean and the maximum position error were reduced by 31%, the RMSE by 34%, and the 95-percentile positioning errors by 52%. Full article

(This article belongs to the Special Issue Indoor Positioning Techniques)

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17 pages, 4098 KiB

Open AccessArticle

Many Ways Lead to the Goal—Possibilities of Autonomous and Infrastructure-Based Indoor Positioning

by Hossein Shoushtari, Thomas Willemsen and Harald Sternberg

Electronics 2021, 10(4), 397; https://doi.org/10.3390/electronics10040397 - 5 Feb 2021

Cited by 7 | Viewed by 2513

Abstract

There are many ways to navigate in Global Navigation Satellite System-(GNSS) shaded areas. Reliable indoor pedestrian navigation has been a central aim of technology researchers in recent years; however, there still exist open challenges requiring re-examination and evaluation. In this paper, a novel [...] Read more.

There are many ways to navigate in Global Navigation Satellite System-(GNSS) shaded areas. Reliable indoor pedestrian navigation has been a central aim of technology researchers in recent years; however, there still exist open challenges requiring re-examination and evaluation. In this paper, a novel dataset is used to evaluate common approaches for autonomous and infrastructure-based positioning methods. The autonomous variant is the most cost-effective realization; however, realizations using the real test data demonstrate that the use of only autonomous solutions cannot always provide a robust solution. Therefore, correction through the use of infrastructure-based position estimation based on smartphone technology is discussed. This approach invokes the minimum cost when using existing infrastructure, whereby Pedestrian Dead Reckoning (PDR) forms the basis of the autonomous position estimation. Realizations with Particle Filters (PF) and a topological approach are presented and discussed. Floor plans and routing graphs are used, in this case, to support PDR positioning. The results show that the positioning model loses stability after a given period of time. Fifth Generation (5G) mobile networks can enable this feature, as well as a massive number of use-cases, which would benefit from user position data. Therefore, a fusion concept of PDR and 5G is presented, the benefit of which is demonstrated using the simulated data. Subsequently, the first implementation of PDR with 5G positioning using PF is carried out. Full article

(This article belongs to the Special Issue Indoor Positioning Techniques)

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15 pages, 3207 KiB

Open AccessArticle

A Semi-Simulated RSS Fingerprint Construction for Indoor Wi-Fi Positioning

by Yuan Yang, Peng Dai, Haoqian Huang, Manyi Wang and Yujin Kuang

Electronics 2020, 9(10), 1568; https://doi.org/10.3390/electronics9101568 - 24 Sep 2020

Cited by 9 | Viewed by 2577

Abstract

Fingerprinting-based Wi-Fi positioning has increased in popularity due to its existing infrastructure and wide coverage. However, in the offline phase of fingerprinting positioning, the construction and maintenance of a Received Signal Strength (RSS) fingerprint database yield high labor. Moreover, the sparsity [...] Read more.

Fingerprinting-based Wi-Fi positioning has increased in popularity due to its existing infrastructure and wide coverage. However, in the offline phase of fingerprinting positioning, the construction and maintenance of a Received Signal Strength (RSS) fingerprint database yield high labor. Moreover, the sparsity and stability of RSS fingerprinting datasets can be the most dominant error sources. This work proposes a minimally Semi-simulated RSS Fingerprinting (SS-RSS) method to generate and maintain dense fingerprints from real spatially coarse RSS acquisitions. This work simulates dense fingerprints exploring the cosine similarity of the directions to Wi-Fi access points (APs), rather than only using either a log-distance path-loss model, a quadratic polynomial fitting, or a spatial interpolation method. Real-world experiment results indicate that the semi-simulated method performs better than the coarse fingerprints and close to the real dense fingerprints. Particularly, the model of RSS measurements, the ratio of the simulated fingerprints to all fingerprints, and a two dimensions (2D) spatial distribution have been analyzed. The average positioning accuracy achieves up to 1.01 m with 66.6% of the semi-simulation ratio. The SS-RSS method offers a solution for coarse fingerprint-based positioning to perform a fine resolution without a time-consuming site-survey. Full article

(This article belongs to the Special Issue Indoor Positioning Techniques)

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28 pages, 8619 KiB

Open AccessArticle

3D Multiple Sound Source Localization by Proposed Cuboids Nested Microphone Array in Combination with Adaptive Wavelet-Based Subband GEVD

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

Electronics 2020, 9(5), 867; https://doi.org/10.3390/electronics9050867 - 23 May 2020

Cited by 2 | Viewed by 3171

Abstract

Sound source localization is one of the applicable areas in speech signal processing. The main challenge appears when the aim is a simultaneous multiple sound source localization from overlapped speech signals with an unknown number of speakers. Therefore, a method able to estimate [...] Read more.

Sound source localization is one of the applicable areas in speech signal processing. The main challenge appears when the aim is a simultaneous multiple sound source localization from overlapped speech signals with an unknown number of speakers. Therefore, a method able to estimate the number of speakers, along with the speaker’s location, and with high accuracy is required in real-time conditions. The spatial aliasing is an undesirable effect of the use of microphone arrays, which decreases the accuracy of localization algorithms in noisy and reverberant conditions. In this article, a cuboids nested microphone array (CuNMA) is first proposed for eliminating the spatial aliasing. The CuNMA is designed to receive the speech signal of all speakers in different directions. In addition, the inter-microphone distance is adjusted for considering enough microphone pairs for each subarray, which prepares appropriate information for 3D sound source localization. Subsequently, a speech spectral estimation method is considered for evaluating the speech spectrum components. The suitable spectrum components are selected and the undesirable components are denied in the localization process. The speech information is different in frequency bands. Therefore, the adaptive wavelet transform is used for subband processing in the proposed algorithm. The generalized eigenvalue decomposition (GEVD) method is implemented in sub-bands on all nested microphone pairs, and the probability density function (PDF) is calculated for estimating the direction of arrival (DOA) in different sub-bands and continuing frames. The proper PDFs are selected by thresholding on the standard deviation (SD) of the estimated DOAs and the rest are eliminated. This process is repeated on time frames to extract the best DOAs. Finally, K-means clustering and silhouette criteria are considered for DOAs classification in order to estimate the number of clusters (speakers) and the related DOAs. All DOAs in each cluster are intersected for estimating the position of the 3D speakers. The closest point to all DOA planes is selected as a speaker position. The proposed method is compared with a hierarchical grid (HiGRID), perpendicular cross-spectra fusion (PCSF), time-frequency wise spatial spectrum clustering (TF-wise SSC), and spectral source model-deep neural network (SSM-DNN) algorithms based on the accuracy and computational complexity of real and simulated data in noisy and reverberant conditions. The results show the superiority of the proposed method in comparison with other previous works. Full article

(This article belongs to the Special Issue Indoor Positioning Techniques)

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23 pages, 2881 KiB

Open AccessFeature PaperArticle

Multi-Sensor Accurate Forklift Location and Tracking Simulation in Industrial Indoor Environments

by Valentín Barral, Pedro Suárez-Casal, Carlos J. Escudero and José A. García-Naya

Electronics 2019, 8(10), 1152; https://doi.org/10.3390/electronics8101152 - 12 Oct 2019

Cited by 33 | Viewed by 6768

Abstract

Location and tracking needs are becoming more prominent in industrial environments nowadays. Process optimization, traceability or safety are some of the topics where a positioning system can operate to improve and increase the productivity of a factory or warehouse. Among the different options, [...] Read more.

Location and tracking needs are becoming more prominent in industrial environments nowadays. Process optimization, traceability or safety are some of the topics where a positioning system can operate to improve and increase the productivity of a factory or warehouse. Among the different options, solutions based on ultra-wideband (UWB) have emerged during recent years as a good choice to obtain highly accurate estimations in indoor scenarios. However, the typical harsh wireless channel conditions found inside industrial environments, together with interferences caused by workers and machinery, constitute a challenge for this kind of system. This paper describes a real industrial problem (location and tracking of forklift trucks) that requires precise internal positioning and presents a study on the feasibility of meeting this challenge using UWB technology. To this end, a simulator of this technology was created based on UWB measurements from a set of real sensors. This simulator was used together with a location algorithm and a physical model of the forklift to obtain estimations of position in different scenarios with different obstacles. Together with the simulated UWB sensor, an additional inertial sensor and optical sensor were modeled in order to test its effect on supporting the location based on UWB. All the software created for this work is published under an open-source license and is publicly available. Full article

(This article belongs to the Special Issue Indoor Positioning Techniques)

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Graphical abstract

19 pages, 5053 KiB

Open AccessArticle

Data Augmentation Schemes for Deep Learning in an Indoor Positioning Application

by Rashmi Sharan Sinha, Sang-Moon Lee, Minjoong Rim and Seung-Hoon Hwang

Electronics 2019, 8(5), 554; https://doi.org/10.3390/electronics8050554 - 17 May 2019

Cited by 37 | Viewed by 4741

Abstract

In this paper, we propose two data augmentation schemes for deep learning architecture that can be used to directly estimate user location in an indoor environment using mobile phone tracking and electronic fingerprints based on reference points and access points. Using a pretrained [...] Read more.

In this paper, we propose two data augmentation schemes for deep learning architecture that can be used to directly estimate user location in an indoor environment using mobile phone tracking and electronic fingerprints based on reference points and access points. Using a pretrained model, the deep learning approach can significantly reduce data collection time, while the runtime is also significantly reduced. Numerical results indicate that an augmented training database containing seven days’ worth of measurements is sufficient to generate acceptable performance using a pretrained model. Experimental results find that the proposed augmentation schemes can achieve a test accuracy of 89.73% and an average location error that is as low as 2.54 m. Therefore, the proposed schemes demonstrate the feasibility of data augmentation using a deep neural network (DNN)-based indoor localization system that lowers the complexity required for use on mobile devices. Full article

(This article belongs to the Special Issue Indoor Positioning Techniques)

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