Adaptive OFDM Radar for Target Detection and Tracking

We develop algorithms to detect and track targets by employing a wideband orthogonal frequency division multiplexing: OFDM) radar signal. The frequency diversity of the OFDM signal improves the sensing performance since the scattering centers of a target resonate variably at different frequencies. In addition, being a wideband signal, OFDM improves the range resolution and provides spectral efficiency. We first design the spectrum of the OFDM signal to improve the radar\u27s wideband ambiguity function. Our designed waveform enhances the range resolution and motivates us to use adaptive OFDM waveform in specific problems, such as the detection and tracking of targets. We develop methods for detecting a moving target in the presence of multipath, which exist, for example, in urban environments. We exploit the multipath reflections by utilizing different Doppler shifts. We analytically evaluate the asymptotic performance of the detector and adaptively design the OFDM waveform, by maximizing the noncentrality-parameter expression, to further improve the detection performance. Next, we transform the detection problem into the task of a sparse-signal estimation by making use of the sparsity of multiple paths. We propose an efficient sparse-recovery algorithm by employing a collection of multiple small Dantzig selectors, and analytically compute the reconstruction performance in terms of the $ell_1$-constrained minimal singular value. We solve a constrained multi-objective optimization algorithm to design the OFDM waveform and infer that the resultant signal-energy distribution is in proportion to the distribution of the target energy across different subcarriers. Then, we develop tracking methods for both a single and multiple targets. We propose an tracking method for a low-grazing angle target by realistically modeling different physical and statistical effects, such as the meteorological conditions in the troposphere, curved surface of the earth, and roughness of the sea-surface. To further enhance the tracking performance, we integrate a maximum mutual information based waveform design technique into the tracker. To track multiple targets, we exploit the inherent sparsity on the delay-Doppler plane to develop an computationally efficient procedure. For computational efficiency, we use more prior information to dynamically partition a small portion of the delay-Doppler plane. We utilize the block-sparsity property to propose a block version of the CoSaMP algorithm in the tracking filter

Innovative Techniques for the Retrieval of Earth’s Surface and Atmosphere Geophysical Parameters: Spaceborne Infrared/Microwave Combined Analyses

With the advent of the first satellites for Earth Observation: Landsat-1 in July 1972 and ERS-1 in May 1991, the discipline of environmental remote sensing has become, over time, increasingly fundamental for the study of phenomena characterizing the planet Earth. The goal of environmental remote sensing is to perform detailed analyses and to monitor the temporal evolution of different physical phenomena, exploiting the mechanisms of interaction between the objects that are present in an observed scene and the electromagnetic radiation detected by sensors, placed at a distance from the scene, operating at different frequencies. The analyzed physical phenomena are those related to climate change, weather forecasts, global ocean circulation, greenhouse gas profiling, earthquakes, volcanic eruptions, soil subsidence, and the effects of rapid urbanization processes. Generally, remote sensing sensors are of two primary types: active and passive. Active sensors use their own source of electromagnetic radiation to illuminate and analyze an area of interest. An active sensor emits radiation in the direction of the area to be investigated and then detects and measures the radiation that is backscattered from the objects contained in that area. Passive sensors, on the other hand, detect natural electromagnetic radiation (e.g., from the Sun in the visible band and the Earth in the infrared and microwave bands) emitted or reflected by the object contained in the observed scene. The scientific community has dedicated many resources to developing techniques to estimate, study and analyze Earth’s geophysical parameters. These techniques differ for active and passive sensors because they depend strictly on the type of the measured physical quantity. In my P.h.D. work, inversion techniques for estimating Earth’s surface and atmosphere geophysical parameters will be addressed, emphasizing methods based on machine learning (ML). In particular, the study of cloud microphysics and the characterization of Earth’s surface changes phenomenon are the critical points of this work

Optical Focusing and Imaging through Scattering Media

Optical techniques, which have been widely used in various fields including bio-medicine, remote sensing, astronomy, and industrial production, play an important role in modern life. Optical focusing and imaging, which correspond to the basic methods of utilizing light, are key to the implementation of optical techniques. In free space or a nearly transparent medium, optical imaging and focusing can be easily realized by using conventional optical elements, such as lenses and mirrors, due to the ballistic propagation of light in these media. However, in scattering media like biological tissue and fog, refractive index inhomogeneities cause diffusive propagation of light that increases with depth, which restricts the use of optical methods in thick, scattering media. Generally speaking, scattering media poses three challenges to optical focusing and imaging: wavefront aberrations, glare, and decorrelation. Wavefront aberrations can randomize light traveling through a scattering medium, disrupt the formation of focus, and break the conjugate relation in imaging. Glare caused by backscattering will largely impair the visibility of imaging, and decorrelation in dynamic media requires systems that counter the effect of scattering to operate faster than the decorrelation time. In this thesis, we explored solutions to the problem of scattering from different aspects. We presented Time Reversal by Analysis of Changing wavefronts from Kinetic targets (TRACK) technique to realize noninvasive optical focusing through a scattering medium. We showed that by taking the difference between time-varying scattering fields caused by a moving object and applying optical phase conjugation, light can be focused back to the location previously occupied by the object. To tackle the decorrelation of living tissue, we built up a fast digital optical phase conjugation (DOPC) system based on FPGA and DMD, which has a response time of 5.3 ms and was the fastest DOPC system in the world before 2017. We demonstrated that the system is fast enough to focus light through 2.3mm-thick living mouse skin. As for glare, inspired by noise canceling headphones, we invented an optical analogue termed coherence gated negation (CGN) technique. CGN can optically cancel out the glare in an active illumination imaging scenario to realize imaging through scattering media, like fog. In the experiment, we suppressed the glare by an order of magnitude and allowed improved imaging of a weak target. Finally, we demonstrated a method to image a moving target through scattering media noninvasively. Its principle roots are in the speckle-correlation-based imaging (SCI) invented by Ori Katz. We improved the technique and extended its application to bright field imaging of a moving target.</p

Advances in Sonar Technology

The demand to explore the largest and also one of the richest parts of our planet, the advances in signal processing promoted by an exponential growth in computation power and a thorough study of sound propagation in the underwater realm, have lead to remarkable advances in sonar technology in the last years.The work on hand is a sum of knowledge of several authors who contributed in various aspects of sonar technology. This book intends to give a broad overview of the advances in sonar technology of the last years that resulted from the research effort of the authors in both sonar systems and their applications. It is intended for scientist and engineers from a variety of backgrounds and even those that never had contact with sonar technology before will find an easy introduction with the topics and principles exposed here

Potential of high-resolution detection and retrieval of precipitation fields from X-band spaceborne synthetic aperture radar over land

Abstract. X-band Synthetic Aperture Radars (X-SARs), able to image the Earth's surface at metric resolution, may provide a unique opportunity to measure rainfall over land with spatial resolution of about few hundred meters, due to the atmospheric moving-target degradation effects. This capability has become very appealing due to the recent launch of several X-SAR satellites, even though several remote sensing issues are still open. This work is devoted to: (i) explore the potential of X-band high-resolution detection and retrieval of rainfall fields from space using X-SAR signal backscattering amplitude and interferometric phase; (ii) evaluate the effects of spatial resolution degradation by precipitation and inhomogeneous beam filling when comparing to other satellite-based sensors. Our X-SAR analysis of precipitation effects has been carried out using both a TerraSAR-X (TSX) case study of Hurricane "Gustav" in 2008 over Mississippi (USA) and a COSMO-SkyMed (CSK) X-SAR case study of orographic rainfall over Central Italy in 2009. For the TSX case study the near-surface rain rate has been retrieved from the normalized radar cross section by means of a modified regression empirical algorithm (MREA). A relatively simple method to account for the geometric effect of X-SAR observation on estimated rainfall rate and first-order volumetric effects has been developed and applied. The TSX-retrieved rain fields have been compared to those estimated from the Next Generation Weather Radar (NEXRAD) in Mobile (AL, USA). The rainfall detection capability of X-SAR has been tested on the CSK case study using the repeat-pass coherence response and qualitatively comparing its signature with ground-based Mt. Midia C-band radar in central Italy. A numerical simulator to represent the effect of the spatial resolution and the antenna pattern of TRMM satellite Precipitation Radar (PR) and Microwave Imager (TMI), using high-resolution TSX-retrieved rain images, has been also set up in order to evaluate the rainfall beam filling phenomenon. As expected, the spatial average can modify the statistics of the high-resolution precipitation fields, strongly reducing its dynamics in a way non-linearly dependent on the rain rate local average value

EXPERIMENTAL EVALUATION OF MODIFIED PHASE TRANSFORM FOR SOUND SOURCE DETECTION

The detection of sound sources with microphone arrays can be enhanced through processing individual microphone signals prior to the delay and sum operation. One method in particular, the Phase Transform (PHAT) has demonstrated improvement in sound source location images, especially in reverberant and noisy environments. Recent work proposed a modification to the PHAT transform that allows varying degrees of spectral whitening through a single parameter, andamp;acirc;, which has shown positive improvement in target detection in simulation results. This work focuses on experimental evaluation of the modified SRP-PHAT algorithm. Performance results are computed from actual experimental setup of an 8-element perimeter array with a receiver operating characteristic (ROC) analysis for detecting sound sources. The results verified simulation results of PHAT- andamp;acirc; in improving target detection probabilities. The ROC analysis demonstrated the relationships between various target types (narrowband and broadband), room reverberation levels (high and low) and noise levels (different SNR) with respect to optimal andamp;acirc;. Results from experiment strongly agree with those of simulations on the effect of PHAT in significantly improving detection performance for narrowband and broadband signals especially at low SNR and in the presence of high levels of reverberation

Estimation of single trial ERPs and EEG phase synchronization with application to mental fatigue

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Potential of High-resolution Detection and Retrieval of Precipitation Fields from X-band Spaceborne Synthetic Aperture Radar over land

X-band Synthetic Aperture Radars (X-SARs), able to image the Earth’s surface at metric resolution, may provide a unique opportunity to measure rainfall over land with spatial resolution of about few hundred meters, due to the atmospheric moving-target degradation effects. This capability has become very appealing due to the recent launch of several X-SAR satellites, even though several remote sensing issues are still open. This work is devoted to: (i) explore the potential of X-band high-resolution detection and retrieval of rainfall fields from space using X-SAR signal backscattering amplitude and interferometric phase; (ii) evaluate the effects of spatial resolution degradation by precipitation and inhomogeneous beam filling when comparing to other satellite-based sensors. Our X-SAR analysis of precipitation effects has been carried out using both a TerraSAR-X (TSX) case study of Hurricane “Gustav” in 2008 over Mississippi (USA) and a COSMO-SkyMed (CSK) X-SAR case study of orographic rainfall over Central Italy in 2009. For the TSX case study the near-surface rain rate has been retrieved from the normalized radar cross section by means of a modified regression empirical algorithm (MREA). A relatively simple method to account for the geometric effect of X-SAR observation on estimated rainfall rate and firstorder volumetric effects has been developed and applied. The TSX-retrieved rain fields have been compared to those estimated from the Next Generation Weather Radar (NEXRAD) in Mobile (AL, USA). The rainfall detection capability of X-SAR has been tested on the CSK case study using the Correspondence to: F. S. Marzano ([email protected]) repeat-pass coherence response and qualitatively comparing its signature with ground-based Mt. Midia C-band radar in central Italy. A numerical simulator to represent the effect of the spatial resolution and the antenna pattern of TRMMsatellite Precipitation Radar (PR) and Microwave Imager (TMI), using high-resolution TSX-retrieved rain images, has been also set up in order to evaluate the rainfall beam filling phenomenon. As expected, the spatial average can modify the statistics of the high-resolution precipitation fields, strongly reducing its dynamics in a way non-linearly dependent on the rain rate local average value

Intelligent video object tracking in large public environments

This Dissertation addresses the problem of video object tracking in large public environments, and was developed within the context of a partnership between ISCTE-IUL and THALES1 object. This partnership aimed at developing a new approach to video tracking, based on a simple tracking algorithm aided by object position estimations to deal with the harder cases of video object tracking. This proposed approach has been applied successfully in the TRAPLE2 project developed at THALES where the main focus is the real-time monitoring of public spaces and the tracking of moving objects (i.e., persons). The proposed low-processing tracking solution woks as follows: after the detection step, the various objects in the visual scene are tracked through their centres of mass (centroids) that, typically, exhibit little variations along close apart video frames. After this step, some heuristics are applied to the results to maintain coherent the identification of the video objects and estimate their positions in cases of uncertainties, e.g., occlusions, which is one of the major novelties proposed in this Dissertation. The proposed approach was tested with relevant test video sequences representing real video monitoring scenes and the obtained results showed that this approach is able to track multiple persons in real-time with reasonable computational power.Esta dissertação aborda o problema do seguimento de objectos vídeo em ambientes públicos de grande dimensão e foi desenvolvida no contexto de uma parceria entre o ISCTE-IUL e a THALES. Esta parceria visou o desenvolvimento de uma nova abordagem ao seguimento de objectos de vídeo baseada num processamento de vídeo simples em conjunto com a estimação da posição dos objectos nos casos mais difíceis de efectuar o seguimento. Esta abordagem foi aplicada com sucesso no âmbito do projecto TRAPLE desenvolvido pela THALES onde um dos principais enfoques é o seguimento de múltiplos objectos de vídeo em tempo real em espaços públicos, tendo como objectivo o seguimento de pessoas que se movam ao longo desse espaço. A solução de baixo nível de processamento proposta funciona do seguinte modo: após o passo de detecção de objectos, os diversos objectos detectados na cena são seguidos através dos seus centros de massa que, normalmente, apresentam poucas variações ao longo de imagens consecutivas de vídeo. Após este passo, algumas heurísticas são aplicadas aos resultados mantendo a identificação dos objectos de vídeo coerente e estimando as suas posições em casos de incertezas (e.g., oclusões) que é uma das principais novidades propostas nesta dissertação. A abordagem proposta foi testada com várias sequências de vídeo de teste representando cenas reais de videovigilância e os resultados obtidos mostraram que esta abordagem é capaz de seguir várias pessoas em tempo real com um nível de processamento moderado

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