Statistical assessment on Non-cooperative Target Recognition using the Neyman-Pearson statistical test

Electromagnetic simulations of a X-target were performed in order to obtain its Radar Cross Section (RCS) for several positions and frequencies. The software used is the CST MWS©. A 1 : 5 scale model of the proposed aircraft was created in CATIA© V5 R19 and imported directly into the CST MWS© environment. Simulations on the X-band were made with a variable mesh size due to a considerable wavelength variation. It is intended to evaluate the Neyman-Pearson (NP) simple hypothesis test performance by analyzing its Receiver Operating Characteristics (ROCs) for two different radar detection scenarios - a Radar Absorbent Material (RAM) coated model, and a Perfect Electric Conductor (PEC) model for recognition purposes. In parallel the radar range equation is used to estimate the maximum range detection for the simulated RAM coated cases to compare their shielding effectiveness (SE) and its consequent impact on recognition. The AN/APG-68(V)9’s airborne radar specifications were used to compute these ranges and to simulate an airborne hostile interception for a Non-Cooperative Target Recognition (NCTR) environment. Statistical results showed weak recognition performances using the Neyman-Pearson (NP) statistical test. Nevertheless, good RCS reductions for most of the simulated positions were obtained reflecting in a 50:9% maximum range detection gain for the PAniCo RAM coating, abiding with experimental results taken from the reviewed literature. The best SE was verified for the PAniCo and CFC-Fe RAMs.Simulações electromagnéticas do alvo foram realizadas de modo a obter a assinatura radar (RCS) para várias posições e frequências. O software utilizado é o CST MWS©. O modelo proposto à escala 1:5 foi modelado em CATIA© V5 R19 e importado diretamente para o ambiente de trabalho CST MWS©. Foram efectuadas simulações na banda X com uma malha de tamanho variável devido à considerável variação do comprimento de onda. Pretende-se avaliar estatisticamente o teste de decisão simples de Neyman-Pearson (NP), analisando as Características de Operação do Receptor (ROCs) para dois cenários de detecção distintos - um modelo revestido com material absorvente (RAM), e outro sendo um condutor perfeito (PEC) para fins de detecção. Em paralelo, a equação de alcance para radares foi usada para estimar o alcance máximo de detecção para ambos os casos de modo a comparar a eficiência de blindagem electromagnética (SE) entre os diferentes revestimentos. As especificações do radar AN/APG-68(V)9 do F-16 foram usadas para calcular os alcances para cada material, simulando uma intercepção hostil num ambiente de reconhecimento de alvos não-cooperativos (NCTR). Os resultados mostram performances de detecção fracas usando o teste de decisão simples de Neyman-Pearson como detector e uma boa redução de RCS para todas as posições na gama de frequências selecionada. Um ganho de alcance de detecção máximo 50:9 % foi obtido para o RAM PAniCo, estando de acordo com os resultados experimentais da bibliografia estudada. Já a melhor SE foi verificada para o RAM CFC-Fe e PAniCo

Novel Models and Algorithms Paving the Road towards RF Convergence

After decades of rapid evolution in electronics and signal processing, the technologies in communications, positioning, and sensing have achieved considerable progress. Our daily lives are fundamentally changed and substantially defined by the advancement in these technologies. However, the trend is challenged by a well-established fact that the spectrum resources, like other natural resources, are gradually becoming scarce. This thesis carries out research in the field of RF convergence, which is regarded as a mean to intelligently exploit spectrum resources, e.g., by finding novel methods of optimising and sharing tasks between communication, positioning, and sensing. The work has been done to closely explore opportunities for supporting the RF convergence. As a supplement for the electromagnetic waves propagation near the ground, ground-to-air channel models are first proposed and analysed, by incorporating the atmospheric effects when the altitude of aerial users is higher than 300 m. The status quos of techniques in communications, positioning, and sensing are separately reviewed, and our newly developments in each field are briefly introduced. For instance, we study the MIMO techniques for interference mitigation on aerial users; we construct the reflected echoes, i.e., the radar receiving, for the joint sensing and communications system. The availability of GNSS signals is of vital importance to the GNSS-enabled services, particularly the life-critical applications. To enhance the resilience of GNSS receivers, the RF fingerprinting based anti-spoofing techniques are also proposed and discussed. Such a guarantee on GNSS and ubiquitous GNSS services drive the utilisation of location information, also needed for communications, hence the proposal of a location-based beamforming algorithm. The superposition coding scheme, as an attempt of the waveform design, is also brought up for the joint sensing and communications. The RF convergence will come with many facets: the joint sensing and communications promotes an efficient use of frequency spectrum; the positioning-aided communications encourage the cooperation between systems; the availability of robust global positioning systems benefits the applications relying on the GNSS service

PHY-layer Security in Cognitive Radio Networks through Learning Deep Generative Models: an AI-based approach

PhD ThesisRecently, Cognitive Radio (CR) has been intended as an intelligent radio endowed with cognition which can be developed by implementing Artificial Intelligence (AI) techniques. Specifically, data-driven Self-Awareness (SA) functionalities, such as detection of spectrum abnormalities, can be effectively implemented as shown by the proposed research. One important application is PHY-layer security since it is essential to establish secure wireless communications against external jamming attacks. In this framework, signals are non-stationary and features from such kind of dynamic spectrum, with multiple high sampling rate signals, are then extracted through the Stockwell Transform (ST) with dual-resolution which has been proposed and validated in this work as part of spectrum sensing techniques. Afterwards, analysis of the state-of-the-art about learning dynamic models from observed features describes theoretical aspects of Machine Learning (ML). In particular, following the recent advances of ML, learning deep generative models with several layers of non-linear processing has been selected as AI method for the proposed spectrum abnormality detection in CR for a brain-inspired, data-driven SA. In the proposed approach, the features extracted from the ST representation of the wideband spectrum are organized in a high-dimensional generalized state vector and, then, a generative model is learned and employed to detect any deviation from normal situations in the analysed spectrum (abnormal signals or behaviours). Specifically, conditional GAN (C-GAN), auxiliary classifier GAN (AC-GAN), and deep VAE have been considered as deep generative models. A dataset of a dynamic spectrum with multi-OFDM signals has been generated by using the National Instruments mm-Wave Transceiver which operates at 28 GHz (central carrier frequency) with 800 MHz frequency range. Training of the deep generative model is performed on the generalized state vector representing the mmWave spectrum with normality pattern without any malicious activity. Testing is based on new and independent data samples corresponding to abnormality pattern where the moving signal follows a different behaviour which has not been observed during training. An abnormality indicator is measured and used for the binary classification (normality hypothesis otherwise abnormality hypothesis), while the performance of the generative models is evaluated and compared through ROC curves and accuracy metrics

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

Design of software defined radio based testbed for smart healthcare

Human Activity Recognition (HAR) help to sense the environment of a human being with an objective to serve a diverse range of human-centric applications in health care, smart-homes and the military. The prevailing detection techniques use ambient sensors, cameras and wearable devices that primarily require strenuous deployment overheads and raise privacy concern as well. Monitoring human activities of daily living is a possible way of describing the functional and health status of a human. Therefore, human activity recognition (HAR) is one of genuine components in personalized life-care and healthcare systems, especially for the elderly and disabled. Recent advances in wireless technologies have demonstrated that a person’s activity can modulate the wireless signal, and enable the transfer of information from a human to an RF transceiver, even when the person does not carry a transmitter. The aim of this PhD project is to design a novel, non-invasive, easily deployable, flexible and scalable test-bed for detecting human daily activities that can help to assess the general physical health of a person based on Software Defined Radios (SDRs). The proposed system also allows us to modify the power level of transceiver model, change the operating frequency, use self-design antennas and change the number of subcarriers in real-time. The results obtained using USRP based wireless sensing for activities of daily living are highly accurate as compared to off-the-shelf wireless devices each time when activities and experiments are performed. This system leverage on the channel state information (CSI) to record the minute movement caused by breathing over orthogonal frequency division multiplexing (OFDM) in multiple sub-carriers. The proposed system combines subject count and activities performed in different classes together, resulting in simultaneous identification of occupancy count and activities performed. Different machine learning algorithms namely K-Nearest Neighbour, Decision Tree, Discriminant Analysis, and Naıve Bayes are used to evaluate the overall performance of the test-bed and achieved a high accuracy. The K-nearest neighbour outperformed all classifiers, providing an accuracy of 89.73% for activity detection and 91.01% for breathing monitoring. A deep learning convolutional neural network is engineered and trained on the CSI data to differentiate multi-subject activities. The proposed system can potentially fulfill the needs of future in-home health activity monitoring and is a viable alternative for monitoring public health and well being

Human activity classification using micro-Doppler signatures and ranging techniques

PhD ThesisHuman activity recognition is emerging as a very import research area due to its potential applications in surveillance, assisted living, and military operations. Various sensors including accelerometers, RFID, and cameras, have been applied to achieve automatic human activity recognition. Wearable sensor-based techniques have been well explored. However, some studies have shown that many users are more disinclined to use wearable sensors and also may forget to carry them. Consequently, research in this area started to apply contactless sensing techniques to achieve human activity recognition unobtrusively. In this research, two methods were investigated for human activity recognition, one method is radar-based and the other is using LiDAR (Light Detection and Ranging). Compared to other techniques, Doppler radar and LiDAR have several advantages including all-weather and all-day capabilities, non-contact and nonintrusive features. Doppler radar also has strong penetration to walls, clothes, trees, etc. LiDAR can capture accurate (centimetre-level) locations of targets in real-time. These characteristics make methods based on Doppler radar and LiDAR superior to other techniques. Firstly, this research measured micro-Doppler signatures of different human activities indoors and outdoors using Doppler radars. Micro-Doppler signatures are presented in the frequency domain to reflect different frequency shifts resulted from different components of a moving target. One of the major differences of this research in relation to other relevant research is that a simple pulsed radar system of very low-power was used. The outdoor experiments were performed in places of heavy clutter (grass, trees, uneven terrains), and confusers including animals and drones, were also considered in the experiments. Novel usages of machine learning techniques were implemented to perform subject classification, human activity classification, people counting, and coarse-grained localisation by classifying the micro-Doppler signatures. For the feature extraction of the micro-Doppler signatures, this research proposed the use of a two-directional twodimensional principal component analysis (2D2PCA). The results show that by applying 2D2PCA, the accuracy results of Support Vector Machine (SVM) and k-Nearest Neighbour (kNN) classifiers were greatly improved. A Convolutional Neural Network (CNN) was built for the target classifications of type, number, activity, and coarse localisation. The CNN model obtained very high classification accuracies (97% to 100%) for the outdoor experiments, which were superior to the results obtained by SVM and kNN. The indoor experiments measured several daily activities with the focus on dietary activities (eating and drinking). An overall classification rate of 92.8% was obtained in activity recognition in a kitchen scenario using the CNN. Most importantly, in nearly real-time, the proposed approach successfully recognized human activities in more than 89% of the time. This research also investigated the effects on the classification performance of the frame length of the sliding window, the angle of the direction of movement, and the number of radars used; providing valuable guidelines for machine learning modeling and experimental setup of micro-Doppler based research and applications. Secondly, this research used a two dimensional (2D) LiDAR to perform human activity detection indoors. LiDAR is a popular surveying method that has been widely used in localisation, navigation, and mapping. This research proposed the use of a 2D LiDAR to perform multiple people activity recognition by classifying their trajectories. Points collected by the LiDAR were clustered and classified into human and non-human classes. For the human class, the Kalman filter was used to track their trajectories, and the trajectories were further segmented and labelled with their corresponding activities. Spatial transformation was used for trajectory augmentation in order to overcome the problem of unbalanced classes and boost the performance of human activity recognition. Finally, a Long Short-term Memory (LSTM) network and a (Temporal Convolutional Network) TCN was built to classify the trajectory samples into fifteen activity classes. The TCN achieved the best result of 99.49% overall accuracy. In comparison, the proposed TCN slightly outperforms the LSTM. Both of them outperform hidden Markov Model (HMM), dynamic time warping (DTW), and SVM with a wide margin

Auroral Image Processing Techniques - Machine Learning Classification and Multi-Viewpoint Analysis

Every year, millions of scientific images are acquired in order to study the auroral phenomena. The accumulated data contain a vast amount of untapped information that can be used in auroral science. Yet, auroral research has traditionally been focused on case studies, where one or a few auroral events have been investigated and explained in detail. Consequently, theories have often been developed on the basis of limited data sets, which can possibly be biased in location, spatial resolution or temporal resolution. Advances in technology and data processing now allow for acquisition and analysis of large image data sets. These tools have made it feasible to perform statistical studies based on auroral data from numerous events, varying geophysical conditions and multiple locations in the Arctic and Antarctic. Such studies require reliable auroral image processing techniques to organize, extract and represent the auroral information in a scientifically rigorous manner, preferably with a minimal amount of user interaction. This dissertation focuses on two such branches of image processing techniques: machine learning classification and multi-viewpoint analysis. Machine learning classification: This thesis provides an in-depth description on the implementation of machine learning methods for auroral image classification; from raw images to labeled data. The main conclusion of this work is that convolutional neural networks stand out as a particularly suitable classifier for auroral image data, achieving up to 91 % average class-wise accuracy. A major challenge is that most auroral images have an ambiguous auroral form. These images can not be readily labeled without establishing an auroral morphology, where each class is clearly defined. Multi-viewpoint analysis: Three multi-viewpoint analysis techniques are evaluated and described in this work: triangulation, shell-projection and 3-D reconstruction. These techniques are used for estimating the volume distribution of artificially induced aurora and the height and horizontal distribution of a newly reported auroral feature: Lumikot aurora. The multi-viewpoint analysis techniques are compared and methods for obtaining uncertainty estimates are suggested. Overall, this dissertation evaluates and describes auroral image processing techniques that require little or no user input. The presented methods may therefore facilitate statistical studies such as: probability studies of auroral classes, investigations of the evolution and formation of auroral structures, and studies of the height and distribution of auroral displays. Furthermore, automatic classification and cataloging of large image data sets will support auroral scientists in finding the data of interest, reducing the needed time for manual inspection of auroral images

Strategies for Devising Automatic Signal Recognition Algorithms in a Shared Radio Environment

In an increasingly congested and complex radio environment interference is to be expected, which poses problems for Automatic Signal Recognition (ASR) systems. This thesis explores strategies for improving ASR performance in the presence of interference. The thesis breaks the overall research question down into a number of subquestions and explores each of these in turn. A Phase-symmetric Cross Recurrence Plot is developed and used to show how a radio signal can be manipulated to separate information about the modulation from the information being carried. The Logarithmic Cyclic frequency Domain Profile is introduced to illustrate how a logarithmic representation can be used for analysing mixtures of signals with very different cyclic frequencies. After defining a canonical ASR system architecture, the concepts of an Ideal Feature and Interference Selectivity are introduced and applied to typical features used in ASR processing. Finally it is shown how these algorithmic developments can be combined in a Bayesian chain implementation that can accommodate a wide variety of feature extraction algorithms. It is concluded that future ASR systems will require features that can handle a wide range of signal types with much higher levels of interference selectivity if they are to achieve acceptable performance in shared spectrum bands. Intelligent segmentation is shown to be a requirement for future ASR systems unless features can be developed that have near ideal performance

PyrSat - Prevention and response to wild fires with an intelligent Earth observation CubeSat

Forest fires are a pervasive and serious problem. Besides loss of life and extensive environmental damage, fires also result in substantial economic losses, not to mention property damage, injuries, displacements and hardships experienced by the affected citizens. This project proposes a low-cost intelligent hyperspectral 3U CubeSat for the production of fire risk and burnt area maps. It applies Machine Learning algorithms to autonomously process images and obtain final data products on-board the satellite for direct transmission to users on the ground. Used in combination with other services such as EFFIS or AFIS, the system could considerably reduce the extent and consequences of forest fires

Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas