Multiple-target tracking using spectropolarimetric imagery

Detection and tracking methods are two hot research topics in the field of multiple target tracking. Often change detection and motion tracking are used to detect and track moving vehicles, but in this thesis new approaches are provided to improve these two aspects. In the detection aspect, a combined detection method is presented to improve target detection techniques. The method of combining RX (Reed-Xiaoli) with change detection has demonstrated good performance in highly cluttered, dynamic ground-based scenes. In the tracking aspect, Kalman filter and Global Nearest Neighbor are applied in motion tracking to predict the location and implement data association respectively. Spectral features are extracted for each vehicle to solve the limitation of motion tracking through feature matching. The Bhattacharyya distance is used as a criterion in the feature matching procedure. Our algorithm has been tested using three sets data. One is a set of multispectral polarimetric imagery acquired by the Multispectral Aerial Passive Polarimeter System (MAPPS). Another two data sets are spectropolarimetric imagery generated by the Digital Imaging and Remote Sensing Image Generation tool. The tracking performance is analyzed by calculating performance metrics: track purity and (Multiple Object Tracking Accuracy ) MOTA. For MAPPS data, the average MOTA and track purity of feature-aided tracking increase 1 percent and 9 percent over those of motion-only tracking respectively. For DIRSIG data with trees, the average track purity of feature-aided tracking in without noise case increases 2 percent over that of motion-only tracking. In this work, we have demonstrated the capability of detection and tracking methods applied in a complex environment

Application of LANDSAT to the management of Delaware's marine and wetland resources

The author has identified the following significant results. LANDSAT data were found to be the best source of synoptic information on the distribution of horizontal water mass discontinuities (fronts) at different portions of the tidal cycle. Distributions observed were used to improve an oil slick movement prediction model for the Delaware Bay. LANDSAT data were used to monitor the movement and dispersion of industrial acid waste material dumped over the continental shelf. A technique for assessing aqueous sediment concentration with limited ground truth was proposed

Matched filter optimization of kSZ measurements with a reconstructed cosmological flow field

We develop and test a new statistical method to measure the kinematic Sunyaev-Zel'dovich (kSZ) effect. A sample of independently detected clusters is combined with the cosmic flow field predicted from a galaxy redshift survey in order to derive a matched filter that optimally weights the kSZ signal for the sample as a whole given the noise involved in the problem. We apply this formalism to realistic mock microwave skies based on cosmological $N$-body simulations, and demonstrate its robustness and performance. In particular, we carefully assess the various sources of uncertainty, cosmic microwave background primary fluctuations, instrumental noise, uncertainties in the determination of the velocity field, and effects introduced by miscentring of clusters and by uncertainties of the mass-observable relation (normalization and scatter). We show that available data (\plk\ maps and the MaxBCG catalogue) should deliver a $7.7\sigma$ detection of the kSZ. A similar cluster catalogue with broader sky coverage should increase the detection significance to $\sim 13\sigma$. We point out that such measurements could be binned in order to study the properties of the cosmic gas and velocity fields, or combined into a single measurement to constrain cosmological parameters or deviations of the law of gravity from General Relativity.Comment: 17 pages, 10 figures, 3 tables. Submitted to MNRAS. Comments are welcome

New Eurocoin: Tracking Economic Growth in Real Time

This paper presents ideas and methods underlying the construction of an indicator that tracks the euro area GDP growth, but, unlike GDP growth, (i) is updated monthly and almost in real time; (ii) is free from hort-run dynamics. Removal of short-run dynamics from a time series, to isolate the mediumlong-run component, can be obtained by a band-pass filter. However, it is well known that band-pass filters, being two-sided, perform very poorly at the end of the sample. New Eurocoin is an estimator of the medium- long-run component of the GDP that only uses contemporaneous values of a large panel of macroeconomic time series, so that no end-of-sample deterioration occurs. Moreover, as our dataset is monthly, New Eurocoin can be updated each month and with a very short delay. Our method is based on generalized principal components that are designed to use leading variables in the dataset as proxies for future values of the GDP growth. As the medium- long-run component of the GDP is observable, although with delay, the performance of New Eurocoin at the end of the sample can be measured.coincident indicator, band-pass filter, large-dataset factor models, generalized principal components

UWB for medical applications

The aim of this project is to be familiarized with UWB radar technology for medical applications. The extremely high-resolution UWB signals together with the low transmit power are good candidates for non-invasive patient monitoring. For instance, breathe rate monitoring. The project will investigate the UWB radar signal detection for breathe monitoring, supported with real experiments. HW equipment consist of TIME DOMAIN® PulsON® 400 Series for Ranging and Communications Application System model and companion Matlab software will support the analysis. ProgrThe respiratory frequency monitoring is an important indicator to the medical field. Also, the need of sensor system solutions for home monitoring is growing as the life expectancy of the world population is increasing. For those reasons, this thesis considers the use of an impulse-radio (IR) UWB radar system to track respiratory frequency and respiratory patterns, as apnoea episodes, in a non-invasive and real-time way. We start our analysis with well-known spectral estimators, like the Periodogram or Bartlett estimator to obtain the first results and insights over the estimation of steady frequencies in an offline regime. Later, we consider the use of adaptive algorithms like the LMS together with AR modelling to monitor the breathing rate transitions and variations. Simulations have been performed to validate and adjust the parameters of the algorithms, balancing between its trade-offs to suit our solution to the problem. Finally, the results of the experiments in different environments are presented meeting the expected requirements and performance of the system.La monitorización de la frecuencia respiratoria es un importante indicador en el campo de la medicina. De la misma manera, la necesidad de soluciones basadas en sistemas de sensores para monitorizar pacientes no hospitalizados en sus hogares crece al mismo ritmo que la esperanza de vida de la población mundial crece. Por esas razones, esta tesis considera el uso de un sistema de radar basado en impulse-radio (IR) UWB para controlar la frecuencia respiratoria, y a la vez, patrones respiratorios, como episodios de apnea, de una manera no invasiva y a tiempo real. Empezamos nuestro análisis con estimadores espectrales como el Periodograma o Estimador Bartlett para obtener los primeros resultados en la estimación de frecuencias estables en una configuración no en tiempo real. Más tarde, consideramos el uso de algoritmos adaptativos como LMS junto a modelado AR para monitorizar las transiciones y variaciones en la frecuencia respiratoria. Se han llevado a cabo simulaciones para validar y ajustar los parámetros de los algoritmos, intentando compensar sus diferentes características para ajustarlos a nuestra problemática. Finalmente, los resultados de experimentos en diferentes escenarios son presentados cumpliendo con los requerimientos y rendimientos esperados del sistema. La monitorització de la freqüència respiratòria es un important indicador en el camp de la medicina. De la mateixa manera, la necessitat de solucions basades en sistemes de sensors per a monitoritzar pacients no hospitalitzats a les seves llars creix a mesura que la esperança de vida de la població mundial creix. Per aquestes raons, aquesta tesi considera l’ús d’un sistema de radar basat en impulse-radio (IR) UWB per a controlar la freqüència respiratòria, i al mateix temps, patrons de respiració, com episodis d’apnea, d’una manera no invasiva i a temps real. Comencem el nostre anàlisi amb estimadors espectrals com el Periodograma o l’Estimador Bartlett per a obtenir els primers resultats en l’estimació de freqüències estables en una configuració no en temps real , per continuar amb, l’ús d’algoritmes adaptatius com LMS junt a modelat AR per a monitoritzar les transicions y variacions en la freqüència respiratòria. Hem dut a terme simulacions per a validar i ajustar els paràmetres dels algoritmes, intentant compensar les seves diferents característiques per a ajustar-los a la nostra problemàtica. Finalment, els resultats de experiments en diferent escenaris son presentats acomplint amb els requisits i rendiments esperats del sistema

Window Functions and Their Applications in Signal Processing

Window functions—otherwise known as weighting functions, tapering functions, or apodization functions—are mathematical functions that are zero-valued outside the chosen interval. They are well established as a vital part of digital signal processing. Window Functions and their Applications in Signal Processing presents an exhaustive and detailed account of window functions and their applications in signal processing, focusing on the areas of digital spectral analysis, design of FIR filters, pulse compression radar, and speech signal processing. Comprehensively reviewing previous research and recent developments, this book: Provides suggestions on how to choose a window function for particular applications Discusses Fourier analysis techniques and pitfalls in the computation of the DFT Introduces window functions in the continuous-time and discrete-time domains Considers two implementation strategies of window functions in the time- and frequency domain Explores well-known applications of window functions in the fields of radar, sonar, biomedical signal analysis, audio processing, and synthetic aperture rada