714 research outputs found
Recent Advances in Indoor Localization Systems and Technologies
Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods
A Survey of 3D Indoor Localization Systems and Technologies
Indoor localization has recently and significantly attracted the interest of the research community mainly due to the fact that Global Navigation Satellite Systems (GNSSs) typically fail in indoor environments. In the last couple of decades, there have been several works reported in the literature that attempt to tackle the indoor localization problem. However, most of this work is focused solely on two-dimensional (2D) localization, while very few papers consider three dimensions (3D). There is also a noticeable lack of survey papers focusing on 3D indoor localization; hence, in this paper, we aim to carry out a survey and provide a detailed critical review of the current state of the art concerning 3D indoor localization including geometric approaches such as angle of arrival (AoA), time of arrival (ToA), time difference of arrival (TDoA), fingerprinting approaches based on Received Signal Strength (RSS), Channel State Information (CSI), Magnetic Field (MF) and Fine Time Measurement (FTM), as well as fusion-based and hybrid-positioning techniques. We provide a variety of technologies, with a focus on wireless technologies that may be utilized for 3D indoor localization such as WiFi, Bluetooth, UWB, mmWave, visible light and sound-based technologies. We critically analyze the advantages and disadvantages of each approach/technology in 3D localization
Learning Robust Radio Frequency Fingerprints Using Deep Convolutional Neural Networks
Radio Frequency Fingerprinting (RFF) techniques, which attribute uniquely identifiable signal distortions to emitters via Machine Learning (ML) classifiers, are limited by fingerprint variability under different operational conditions. First, this work studied the effect of frequency channel for typical RFF techniques. Performance characterization using the multi-class Matthews Correlation Coefficient (MCC) revealed that using frequency channels other than those used to train the models leads to deterioration in MCC to under 0.05 (random guess), indicating that single-channel models are inadequate for realistic operation. Second, this work presented a novel way of studying fingerprint variability through Fingerprint Extraction through Distortion Reconstruction (FEDR), a neural network-based approach for quantifying signal distortions in a relative distortion latent space. Coupled with a Dense network, FEDR fingerprints were evaluated against common RFF techniques for up to 100 unseen classes, where FEDR achieved best performance with MCC ranging from 0.945 (5 classes) to 0.746 (100 classes), using 73% fewer training parameters than the next-best technique
Roadmap on signal processing for next generation measurement systems
Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing. This roadmap presents a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems. It covers a broad spectrum of topics ranging from basic to industrial research, organized in concise thematic sections that reflect the trends and the impacts of current and future developments per research field. Furthermore, it offers guidance to researchers and funding agencies in identifying new prospects.AerodynamicsMicrowave Sensing, Signals & System
Smart Classifiers and Bayesian Inference for Evaluating River Sensitivity to Natural and Human Disturbances: A Data Science Approach
Excessive rates of channel adjustment and riverine sediment export represent societal challenges; impacts include: degraded water quality and ecological integrity, erosion hazards to infrastructure, and compromised public safety. The nonlinear nature of sediment erosion and deposition within a watershed and the variable patterns in riverine sediment export over a defined timeframe of interest are governed by many interrelated factors, including geology, climate and hydrology, vegetation, and land use. Human disturbances to the landscape and river networks have further altered these patterns of water and sediment routing.
An enhanced understanding of river sediment sources and dynamics is important for stakeholders, and will become more critical under a nonstationary climate, as sediment yields are expected to increase in regions of the world that will experience increased frequency, persistence, and intensity of storm events. Practical tools are needed to predict sediment erosion, transport and deposition and to characterize sediment sources within a reasonable measure of uncertainty. Water resource scientists and engineers use multidimensional data sets of varying types and quality to answer management-related questions, and the temporal and spatial resolution of these data are growing exponentially with the advent of automated samplers and in situ sensors (i.e., “big data”). Data-driven statistics and classifiers have great utility for representing system complexity and can often be more readily implemented in an adaptive management context than process-based models. Parametric statistics are often of limited efficacy when applied to data of varying quality, mixed types (continuous, ordinal, nominal), censored or sparse data, or when model residuals do not conform to Gaussian distributions. Data-driven machine-learning algorithms and Bayesian statistics have advantages over Frequentist approaches for data reduction and visualization; they allow for non-normal distribution of residuals and greater robustness to outliers.
This research applied machine-learning classifiers and Bayesian statistical techniques to multidimensional data sets to characterize sediment source and flux at basin, catchment, and reach scales. These data-driven tools enabled better understanding of: (1) basin-scale spatial variability in concentration-discharge patterns of instream suspended sediment and nutrients; (2) catchment-scale sourcing of suspended sediments; and (3) reach-scale sediment process domains. The developed tools have broad management application and provide insights into landscape drivers of channel dynamics and riverine solute and sediment export
Analysis and Detection of Outliers in GNSS Measurements by Means of Machine Learning Algorithms
L'abstract è presente nell'allegato / the abstract is in the attachmen
Indoor Positioning and Navigation
In recent years, rapid development in robotics, mobile, and communication technologies has encouraged many studies in the field of localization and navigation in indoor environments. An accurate localization system that can operate in an indoor environment has considerable practical value, because it can be built into autonomous mobile systems or a personal navigation system on a smartphone for guiding people through airports, shopping malls, museums and other public institutions, etc. Such a system would be particularly useful for blind people. Modern smartphones are equipped with numerous sensors (such as inertial sensors, cameras, and barometers) and communication modules (such as WiFi, Bluetooth, NFC, LTE/5G, and UWB capabilities), which enable the implementation of various localization algorithms, namely, visual localization, inertial navigation system, and radio localization. For the mapping of indoor environments and localization of autonomous mobile sysems, LIDAR sensors are also frequently used in addition to smartphone sensors. Visual localization and inertial navigation systems are sensitive to external disturbances; therefore, sensor fusion approaches can be used for the implementation of robust localization algorithms. These have to be optimized in order to be computationally efficient, which is essential for real-time processing and low energy consumption on a smartphone or robot
Desenvolvimento de metodologias para localização indoor de smartphones com exatidão ao centímetro
Doutoramento em Engenharia ElectrotécnicaThis thesis describes the design and implementation of a reliable
centimeter-level indoor positioning system fully compatible with a conventional
smartphone. The proposed system takes advantage of the
smartphone audio I/O and processing capabilities to perform acoustic
ranging in the audio band using non-invasive audio signals and it has
been developed having in mind applications that require high accuracy,
such as augmented reality, virtual reality, gaming and audio guides. The
system works in a distributed operation mode, i.e. each smartphone is
able to obtain its own position using only acoustic signals. To support
the positioning system, a Wireless Sensor Network (WSN) of synchronized
acoustic beacons is used. To keep the infrastructure in sync we
have developed an Automatic Time Synchronization and Syntonization
(ATSS) protocol with a standard deviation of the sync offset error below
1.25 μs. Using an improved Time Difference of Arrival (TDoA) estimation
approach (which takes advantage of the beacon signals’ periodicity)
and by performing Non-Line-of-Sight (NLoS) mitigation, we were
able to obtain very stable and accurate position estimates with an absolute
mean error of less than 10 cm in 95% of the cases and a mean
standard deviation of 2.2 cm for a position refresh period of 350 ms.Esta tese descreve o projeto e a implementação de um sistema de
localização para ambientes interiores totalmente compatível com um
smartphone convencional. O sistema proposto explora a capacidade
de aquisição de sinais áudio e de processamento do smartphone para
medir distâncias utilizando sinais acústicos na banda do audível; foram
utilizados sinais áudio não-invasivos, i.e. com reduzido impacto perceptual
em humanos. No desenvolvimento deste sistema foram consideradas
aplicações que exigem elevada exatidão, na ordem dos centímetros,
tais como realidade aumentada, realidade virtual, jogos ou
guias virtuais. Utilizou-se uma infraestrutura de faróis de baixo custo
suportada por uma rede de sensores sem fios (RSSF). Para manter
a infraestrutura síncrona, foi desenvolvido um protocolo de sincronização
e sintonização automática, (Automatic Time Synchronization and
Syntonization - ATSS) que garante um desvio padrão do erro de offset
abaixo de 1.25 μs. Cada smartphone efectua medidas MT-TDoA
que posteriormente são utilizadas pelo algoritmo de localização hiperbólica.
As estimativas de posição resultantes são estáveis e precisas,
com um erro médio absoluto menor do que 10 cm em 95% dos casos
e um desvio padrão médio de 2.2 cm, para um período de atualização
de posição de 350 ms
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