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Data emission/acquisition board, power amplifier, signal voltage multiplier, and 200 V DC-bias circuitry, and wired miniature microphone.
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The growing interest for indoor position-based applications and services, as well as ubiquitous computing and location aware information, have led to increasing efforts toward the development of positioning techniques. Many applications require accurate positioning or tracking of people and assets inside buildings, and some market sectors are waiti...
Contexts in source publication
Context 1
... Emission/Acquisition Board: MOTU 828 mk3 (MOTU, Cambridge, Massachusetts, USA). It is provided with ten analog inputs and outputs that can operate at sample rates up to 192 ksamples/s (see Figure 8). The PC connection is realized through FireWire. ...
Context 2
... Device: Here, the MD is mimicked by a wired miniature microphone, amplified, and sampled by the MOTU at 192 ksamples/s (FS = 192 kHz). The microphone is an FG-6163 (Knowles Acoustics, Itasca, Illinois, USA), a micromachined condenser microphone encapsulated in a cylindrical package, 2.6 mm length and diameter, 0.79 mm acoustical receiver window diameter, and 80 mg weight (see Figure 8). This microphone is the same one that equips the MD described in [18]. ...
Citations
... In the following simulations, the objective is to investigate the acoustic field and assess the efficacy of the proposed ranging technique within a standard 4 m × 4 m × 3 m room [32]. Specifically, the simulation results will be carried out for a grid of points forming a vertical section (refer to Section A in the room volume, see Figure 1 in [26]) and a horizontal section positioned midway between the floor and ceiling (refer to Section B in the room volume, Figure 1 in [26]). ...
... The signal employed in the simulations is a linear chirp spanning a bandwidth of 30-50 kHz and lasting 5.12 milliseconds [11,32]. For simulation purposes, the signal was sampled at a rate of f S = 1 MHz. ...
... In Figure 4a in [26], the ranging error was evaluated for D = 25 mm. It shows the ranging error along a rectangular vertical section (section of the room volume, Figure 1) of 3 m height and 4 m base passing through the center of the transducer, equal to the vertical section of the typical office room taken as a reference in some positioning works [32,38], while Figure 7b in [26] shows the behavior of the ranging error on a horizontal section of 4 m × 4 m at z = 1.5 m, or halfway between the ground and the ceiling. In both the figures, the grid pitch is 5 cm in the x and y directions. ...
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).
... To achieve robust and reliable indoor positioning performance, systems utilising easily-employed signal transmitters and receivers were proposed. Technologies such as Ultra-wideband (UWB) (Poulose and Han 2020;Ridolfi et al. 2021), Bluetooth Low Energy (BLE) (Bai et al. 2020;Spachos and Plataniotis 2020), Ultrasonic (Carotenuto et al. 2020;Lindo et al. 2015) and LED (Hassan et al. 2015;Rahman, Haque, and Kim 2011) were leveraged in the literature for indoor tracking. Due to the omnipresent infrastructure of WiFi-enabled devices, WiFi fingerprinting has become one of the most popular approaches for indoor positioning Xue et al. 2020). ...
... Sun et al., 2022), ultra-wideband (UWB) (Ardiansyah, Nugraha, Han, Deokjai, & Kim, 2019;B. Li, Zhao, & Sandoval, 2020), ultrasonic (Carotenuto, Merenda, Iero, Della Corte, & F., 2020;Chen et al., 2021), computer vision (Jeong, Min, & Park, 2021;Jin, Ko, & Lee, 2018), visible light (Bai et al., 2021;Bakar, Glass, Tee, Alam, & Legg, 2021), pseudo-satellite (Y. Sun, Wang, & Chen, 2021;B. ...
Wireless fidelity (WiFi) indoor positioning has attracted the attention of thousands of researchers. It faces many challenges, and the primary problem is the low positioning accuracy, which hinders its widespread applications.
To improve the accuracy, we propose a WiFi indoor positioning algorithm based on support vector regression (SVR) optimized by particle swarm optimization (PSO), termed PSOSVRPos. SVR algorithm devotes itself to solving localization as a regression problem by building the mapping between signal features and spatial coordinates in high dimensional space. PSO algorithm concentrates on the global-optimal parameter estimation of the SVR model. The positioning experiment is conducted on an open dataset (1511 samples, 154 features). The PSOSVRPos algorithm could achieve positioning accuracy with a mean absolute error of 1.040 meters, a root mean square error (RMSE) of 0.863 meters and errors within 1 meter of 59.8%.
Experimental results indicate that the PSOSVRPos algorithm is a precise approach for WiFi indoor positioning as it reduces the RMSE (35%) and errors within 1 m (14%) compared with state-of-the-art algorithms such as convolutional neural network (CNN) based methods.
... The low speed of ultrasonic waves in the air results in high time-of-flight accuracy and, consequently, high positioning accuracy. The system used is based on the presence of four ultrasonic beacons and one or more mobile receivers [31]. Beacons transmit a periodic ultrasonic sequence. ...
The increase in the elderly population has led to the need for new medical, social, and care services, resulting in a significant rise in health costs and the number of health workers involved. For example, IoT (Internet of Things) and wearable technologies can help contain healthcare spending and enable better living conditions of elderly. Moreover, nanotechnologies such as MEMS (micro-electromechanical system) that offer the advantage of small size, negligible need for power and motion acquisition are of considerable benefit. These technologies are able to detect and signal dangerous situations in order to ensure immediate action. In this article, we present an implementation of an IoT application on a latest-generation microcontroller. Kinematics and environmental data are transferred to a CNN (Convolutional Neural Network) to recognize the daily activities of the elderly in their homes or nursing homes. Finally, to determine the position of subjects, we associate the prototype with a positioning system on the ultrasonic platform. Finally, applying the Edge Machine Learning technique, we developed an application on the STM32L475VG microprocessor on which motion acquisition and activity recognition functions are activated.
... A contrasting design, in which the handheld devices are arranged as receivers for some predefined emitters, is another popular design because mobile phone users always need the real-time display of the mobile phones' monitors [24,25]. The positioning algorithm based on TOA or TDOA methods requires that multisensors be used, i.e., the total costs will be higher than those using the single sensor design; besides, the TOA or TDOA methods are degraded by four main factors: 1. background noise, 2. the multi-path effect [26,27], 3. non line-of-sight propagation [28,29], and 4. mobile synchronization recovery [30,31]. ...
An indoor positioning design developed for mobile phones by integrating a single microphone sensor, an H2 estimator, and tagged sound sources, all with distinct frequencies, is proposed in this investigation. From existing practical experiments, the results summarize a key point for achieving a satisfactory indoor positioning: The estimation accuracy of the instantaneous sound pressure level (SPL) that is inevitably affected by random variations of environmental corruptions dominates the indoor positioning performance. Following this guideline, the proposed H2 estimation design, accompanied by a sound pressure level model, is developed for effectively mitigating the influences of received signal strength (RSS) variations caused by reverberation, reflection, refraction, etc. From the simulation results and practical tests, the proposed design delivers a highly promising indoor positioning performance: an average positioning RMS error of 0.75 m can be obtained, even under the effects of heavy environmental corruptions.
... Ultrasonic sensors have been widely used in the fields of distance measurements [1][2][3], positioning [4][5][6], non-destructive testing [7,8], and human body inspection [9][10][11]. In particular, they have been used from the past to implement intelligent safety technologies such as autonomous driving of automobiles and front and rear collision avoidance [12][13][14], because of their centimeter to sub-centimeter accuracy in air, low cost, and low hardware complexity. ...
Various sensors are embedded in automobiles to implement intelligent safety technologies such as autonomous driving and front–rear collision avoidance technology. In particular, ultrasonic sensors have been used in the past because they have an accuracy of centimeters to sub-centimeters in air despite their low cost and low hardware complexity. Recently, the crosstalk problem between ultrasonic sensors has been raised because the number of ultrasonic sensors in the unit space has increased as the number of vehicles increases. Various studies have been conducted to solve the crosstalk, but a demodulation error occurs when signals overlap. Therefore, in this paper, we propose a method that is robust to ultrasonic signal overlap, is robust even at shorter code length, and has reduced time of flight (TOF) error compared to the existing method by applying frequency sweep keying modulation based on code division multiple access (CDMA). As a result of the experiment, the code was detected accurately regardless of the overlap ratio of the two signals, and it was robust even in situations where the power of the two signals was different. In addition, it shows an accurate TOF estimation even if the ID code length is shorter than the existing on–off-keying, frequency shift keying, and phase shift keying methods.
... Ultrasound imaging is employed in a wide variety of applications, including sonar [9][10][11], non-destructive evaluation (NDE) [12][13][14], indoor positioning systems (IPS) [15,16] and biometric recognition [17][18][19]. However, its best known application is medical diagnostics, where it has been found to be very attractive and interesting compared to other imaging modalities, such as magnetic resonance (MR), X-rays and computed tomography(CT), because it enables the acquisition of organ images at low cost and in a safe and noninvasive way. ...
Machine learning (ML) methods are pervading an increasing number of fields of application because of their capacity to effectively solve a wide variety of challenging problems. The employment of ML techniques in ultrasound imaging applications started several years ago but the scientific interest in this issue has increased exponentially in the last few years. The present work reviews the most recent (2019 onwards) implementations of machine learning techniques for two of the most popular ultrasound imaging fields, medical diagnostics and non-destructive evaluation. The former, which covers the major part of the review, was analyzed by classifying studies according to the human organ investigated and the methodology (e.g., detection, segmentation, and/or classification) adopted, while for the latter, some solutions to the detection/classification of material defects or particular patterns are reported. Finally, the main merits of machine learning that emerged from the study analysis are summarized and discussed.
... In addition, the relatively low cost of ultrasonic transducers is user-friendly for engineering applications such as reversing radar and construction surveys. Therefore, ultrasonic ranging is suitable for small range (the distance is usually less than 10 m) and high-precision (the accuracy can reach the level of millimeter generally) non-contact distance measurement, and the derived two-dimensional positioning [10] and three-dimensional positioning [11,12], object shape recognition [13] and multi-sensor fusion trajectory measurement [14], etc. ...
Ultrasonic ranging has been widely used in automobiles, unmanned aerial vehicles (UAVs), robots and other fields. With the appearance of micromachined ultrasonic transducers (MUTs), the application of ultrasonic ranging technology presents a more extensive trend. This review focuses on ultrasonic ranging technology and its development history and future trend. Going through the state-of-the-art ultrasonic ranging methods, this paper covers the principles of each method, the signal processing methodologies, the overall system performance as well as key ultrasonic transducer parameters. Moreover, the error sources and compensation methods of ultrasonic ranging systems are discussed. This review aims to give an overview of the ultrasonic ranging technology including its current development and challenges.
... Ultrasonic [31,32] Ultrasonic positioning accuracy is very high. ...
... Ultrasonic positioning technology is achieved by installing multiple ultrasonic speakers in a room and detecting ultrasonic signals through terminal microphones for indoor positioning [31,32]. The location of the terminal device can be estimated by the difference in arrival time of different sound waves. ...
Due to the recent COVID-19 pandemic, many people have faced in-home isolation, as every suspected patient must stay at home. The behavior of such isolated people needs to be monitored to ensure that they are staying at home. Using a camera is a very practical method. However, smart bracelets are more convenient when personal privacy is a concern or when the blood oxygen value or heart rate must be monitored. In this study, a low-cost indoor positioning system that uses a Bluetooth beacon, a smart bracelet, and an embedded system is proposed. In addition to monitoring whether a person living alone is active in a specific environment and tracking the heart rate or blood oxygen value under particular conditions, this system can also send early warning signals to specific observation units or relatives through instant messaging software.
... Therefore, a close synchronization between the local clocks of the transmitter and receiver is required to achieve accurate distance measurements. Several synchronization techniques have been proposed in the literature [20], [21]. ...
... A laboratory-level positioning system [21] provides the ultrasonic signal data and the related frame by frame sensor positions. ...
Indoor positioning of objects and people is becoming
of great importance in the Internet of Things (IoT), in-home
automation, and navigation in malls, airports, or very large
buildings. Positioning is determined by multiple distance
measurements between reference points and sensors. Distance
measurement uses the time of flight of an ultrasonic signal
traveling from an emitter to receiving sensors. This requires close
synchronization between the emitter and the sensors and a sharp
time resolution of the time of arrival (TOA) of the ultrasonic
signal. Usually, TOA is detected using cross-correlation processing
requiring significant computational resources at the sensors level.
In this work, the synchronization is done using the RFID standard
protocol features. The TOA detection is performed firstly by
training off-line a Machine Learning model using as input the
peaks indexes of the ultrasonic signal received and the output of a
cross-correlation based positioning system, as ground-truth. In a
second phase, the positioning is evaluated and tested on-board
using the previously trained model on a microcontroller. The
system architecture is presented and experimental results on the
positioning accuracy are shown accordingly. Results show a mean
positioning error below 25 cm in 95% of the positionings in a
typical room.
