On the Application of the Baum-Welch Algorithm for Modeling the Land Mobile Satellite Channel

Accurate channel models are of high importance for the design of upcoming mobile satellite systems. Nowadays most of the models for the LMSC are based on Markov chains and rely on measurement data, rather than on pure theoretical considerations. A key problem lies in the determination of the model parameters out of the observed data. In this work we face the issue of state identification of the underlying Markov model whose model parameters are a priori unknown. This can be seen as a HMM problem. For finding the ML estimates of such model parameters the BW algorithm is adapted to the context of channel modeling. Numerical results on test data sequences reveal the capabilities of the proposed algorithm. Results on real measurement data are finally presented.Comment: IEEE Globecom 201

Modeling of the Land Mobile Satellite Channel considering the Terminal’s Driving Direction

A precise characterization of the Land Mobile Satellite (LMS) channel, that is, the channel between a satellite and a mobile terminal, is of crucial importance while designing a satellite-based communication system. State-of-the-art statistical LMS channel models offer the advantage of requiring only a few input parameters, which include the environment type and the elevation angle of the satellite. However, the azimuth angle relative to the driving direction of the mobile terminal is usually ignored, as its proper modeling requires either an extensive measurement campaign or a significant effort from the user, as a precise geometrical description of the scenario is required. In this contribution we show that the impact of the driving direction on the channel statistics is not negligible and requires to be modeled explicitly. Moreover, we propose a statistical LMS channel model whose parameters are obtained via an image-based state estimation method. The image-based method is verified by a comparison with measured radio frequency signal levels. The proposed method allows obtaining a complete statistical description of the channel for arbitrary elevation and azimuth angles

On the use of autonomous unmanned vehicles in response to hazardous atmospheric release incidents

Recent events have induced a surge of interest in the methods of response to releases of hazardous materials or gases into the atmosphere. In the last decade there has been particular interest in mapping and quantifying emissions for regulatory purposes, emergency response, and environmental monitoring. Examples include: responding to events such as gas leaks, nuclear accidents or chemical, biological or radiological (CBR) accidents or attacks, and even exploring sources of methane emissions on the planet Mars. This thesis presents a review of the potential responses to hazardous releases, which includes source localisation, boundary tracking, mapping and source term estimation. [Continues.]</div

Characterization of Fine Particulate Matter (PM) and Secondary PM Precursor Gases in the Mexico City Metropolitan Area

This project was one of three collaborating grants funded by DOE/ASP to characterize the fine particulate matter (PM) and secondary PM precursors in the Mexico City Metropolitan Area (MCMA) during the MILAGRO Campaign. The overall effort of MCMA-2006, one of the four components, focused on i) examination of the primary emissions of fine particles and precursor gases leading to photochemical production of atmospheric oxidants and secondary aerosol particles; ii) measurement and analysis of secondary oxidants and secondary fine PM production, with particular emphasis on secondary organic aerosol (SOA), and iii) evaluation of the photochemical and meteorological processes characteristic of the Mexico City Basin. The collaborative teams pursued the goals through three main tasks: i) analyses of fine PM and secondary PM precursor gaseous species data taken during the MCMA-2002/2003 campaigns and preparation of publications; ii) planning of the MILAGRO Campaign and deployment of the instrument around the MCMA; and iii) analysis of MCMA-2006 data and publication preparation. The measurement phase of the MILAGRO Campaign was successfully completed in March 2006 with excellent participation from the international scientific community and outstanding cooperation from the Mexican government agencies and institutions. The project reported here was led by the Massachusetts Institute of Technology/Molina Center for Energy and the Environment (MIT/MCE2) team and coordinated with DOE/ASP-funded collaborators at Aerodyne Research Inc., University of Colorado at Boulder and Montana State University. Currently 24 papers documenting the findings from this project have been published. The results from the project have improved significantly our understanding of the meteorological and photochemical processes contributing to the formation of ozone, secondary aerosols and other pollutants. Key findings from the MCMA-2003 include a vastly improved speciated emissions inventory from on-road vehicles: the MCMA motor vehicles produce abundant amounts of primary PM, elemental carbon, particle-bound polycyclic aromatic hydrocarbons, carbon monoxide and a wide range of air toxics; the feasibility of using eddy covariance techniques to measure fluxes of volatile organic compounds in an urban core and a valuable tool for validating local emissions inventory; a much better understanding of the sources and atmospheric loadings of volatile organic compounds; the first spectroscopic detection of glyoxal in the atmosphere; a unique analysis of the high fraction of ambient formaldehyde from primary emission sources; characterization of ozone formation and its sensitivity to VOCs and NOx; a much more extensive knowledge of the composition, size distribution and atmospheric mass loadings of both primary and secondary fine PM, including the fact that the rate of MCMA SOA production greatly exceeded that predicted by current atmospheric models; evaluations of significant errors that can arise from standard air quality monitors for O3 and NO2; and the implementation of an innovative Markov Chain Monte Carlo method for inorganic aerosol modeling as a powerful tool to analyze aerosol data and predict gas phase concentrations where these are unavailable. During the MILAGRO Campaign the collaborative team utilized a combination of central fixed sites and a mobile laboratory deployed throughout the MCMA to representative urban and boundary sites to measure trace gases and fine particles. Analysis of the extensive 2006 data sets has confirmed the key findings from MCMA-2002/2003; additionally MCMA-2006 provided more detailed gas and aerosol chemistry and wider regional scale coverage. Key results include an updated 2006 emissions inventory; extension of the flux system to measure fluxes of fine particles; better understanding of the sources and apportionment of aerosols, including contribution from biomass burning and industrial sources; a comprehensive evaluation of metal containing particles in a complex urban environment; identification of a close correlation between the rate of production of SOA and “Odd Oxygen” (O3 + NO3) and primary organic PM with CO in the urban plume; a more sophisticated understanding of the relationship between ozone formation and ozone precursors: while ozone production in the urban area is VOC-limited, the response is mostly NOx-limited in the surrounding mountain. Comparison of the findings from 2003 and 2006 also confirm that the VOC levels have decreased during the three-year period, while NOx levels remain the same. The results from the 2002/2003 and 2006 have been presented at international conferences and communicated to Mexican government officials. In addition, a large number of graduate students and post-doctoral associates were involved in the project. All data sets and publications are available to the scientific community

Rich probabilistic models for semantic labeling

Das Ziel dieser Monographie ist es die Methoden und Anwendungen des semantischen Labelings zu erforschen. Unsere Beiträge zu diesem sich rasch entwickelten Thema sind bestimmte Aspekte der Modellierung und der Inferenz in probabilistischen Modellen und ihre Anwendungen in den interdisziplinären Bereichen der Computer Vision sowie medizinischer Bildverarbeitung und Fernerkundung

Development of Real-Time Surface Water Abstraction Management Tools

Efficient use of available water resources to meet demand, whilst maintaining the quality of the aquatic environment has become increasingly important. Water quality challenges associated with diffuse agricultural pollutions have also become widely recognized problems globally. This thesis presents the development of new approaches to improve surface water abstraction management with a view to mitigate the challenges associated with increasing pressures on availability of water resources for public water supply and diffuse agricultural pollution. The first part of the thesis presents the development of a real-time surface water abstraction management scheme that integrates a conceptual rainfall-runoff model, a Bayesian inference based uncertainty analysis tool and a water resources management model that incorporates various operating rules to represent real-world operational constraints. The developed approach enables efficient utilization of available water resources and thus provides improved capability to deal with emerging issues of increasing demand, climate adaptation planning and associated policy reforms. The second part of the thesis describes the development of a new travel time based physically distributed metaldehyde prediction model, which enables water infrastructure operators to consider informed surface water abstraction decisions. Metaldehyde is a soluble synthetic aldehyde pesticide used globally in agriculture and has caused recent concerns due to high observed levels in surface waters utilized for potable water supply. The model provides new approach to represent spatially and temporally disaggregated runoff generation, routing and build-up/wash-off processes using a grid based structure in a GIS environment. Furthermore, a state-of-the-art Monte Carlo based spatial uncertainty analysis tool is employed to assess uncertainties in the metaldehyde prediction model. The structure of the metaldehyde model combined with the availability of high spatiotemporal resolution data has enabled the application of spatial uncertainty analysis of the catchment scale metaldehyde model, which is currently lacking in water quality modelling studies

On Channel Modelling for Land Mobile Satellite Reception

In modernen Satellitenrundfunksystemen werden Methoden wie Zeitdiversität (Empfang von zeitlich verteilter Information) und Winkeldiversität (Empfang von mehreren Satelliten mit unterschiedlichen Positionen) angewandt, um die geforderte Dienstequalität für den mobilen Empfang zu gewährleisten. Zur Untersuchung der Ausbreitungseffekte des landmobilen Satellitenkanals sowohl der Wirksamkeit von Diversität werden statistische Modelle benötigt, die den zeitlichen Signalschwund des Empfangssignals nachbilden. In der vorliegenden Arbeit wird ein Kanalmodell für den Mehrsatellitenempfang entwickelt, welches genaue Versorgungsvorhersagen mit Zeit- und Winkeldiversität erlaubt.Grundlage ist ein Einsatellitenmodell, welches großräumige Schwankungen im Kanal durchdie Zustände ’gut’ und ’schlecht’ definiert, und den langsamen und schnellen Signalschwund gemäß einer variablen Loo-Verteilung beschreibt, deren Parameter nach jedem Zustandswechsel stochastisch bestimmt werden. Für die Zustandsmodellierung mit zwei Satelliten wird ein 'semi-Markov Modell für korrellierte Zustandssequenzen' erarbeitet. Damit können, unter Berücksichtigung der Statistiken der Einzelkanäle und deren Korrelationskoeffizient, die Zustandswahrscheinlichkeiten und-längen exakt simuliert werden. Für die Zustandsmodellierung mit drei Satelliten wird ein 'Master-Slave'-Ansatz entwickelt. Dabei sind die Zustandssequenzen zweier ’Slaves’ bedingt abhängig zur ’Master’-Sequenz. Der ’Master-Slave’-Ansatz ermöglicht die Parametrisierung eines Dreisatellitenmodells. Zur Beschreibung des langsamen und schnellen Signalschwunds im Mehrsatellitenkanal wirddie Wechselbeziehung zwischen synchron gemessenen Satellitensignalen näher untersucht.Es stellt sich heraus dass weitere Signalkorrelationen berücksichtigt werden sollten, dieerstmalig im neuen LMS-Kanalmodell implementiert werden. Die Simulationssergebnisse werden in Statistiken erster und zweiter Ordnung den Messdaten gegenübergestellt. Im Vergleich zu bestehenden Modellen werden Verbesserungen nach Berücksichtigung von Diversität deutlich. Die Parameter für das Mehrsatellitenkanalmodell wurden aus umfassenden Messkampagnen abgeleitet und gewährleisten die Signalsimulation für verschiedene Umgebungen und Satellitenpositionen. Abschließend wird das Kanalmodell für einen ersten Vergleich verschiedener Satellitenkonfigurationen mit Zeit- und Winkeldiversität angewandt.Modern digital satellite broadcasting systems combine time diversity (i.e. information is spread over a certain time interval) with angle diversity (i.e. information is received from multiple satellites in different orbital positions) to ensure uninterrupted service for mobile receivers over large areas. For assessing propagation effects of the land mobile satellite (LMS) channel and to study the efficacy of diversity, statistical models are required which generate time series of the received fading signal. In this thesis a narrowband LMS channel model for multi-satellite reception is developed focusing on accurate coverage prediction under consideration of angle- and time diversity. Basis is an existing single-satellite model, which describes large-scale signal variations of the channel by 'good' and 'bad' states, and models slow- and fast signal variations according to a versatile Loo distribution, which parameters are selected randomly when the channel enters a new state. For dual-satellite state modelling, a semi-Markov model for correlated state sequences is developed. It provides an accurate state probability and state duration modelling by considering the statistics of separate channels and their correlation coefficient. For the state modelling with three satellites, a new Master-Slave concept is introduced. Therefore, state sequences of slave satellites are conditioned by an existing master state sequence. The great advantage is that Master-Slave makes the parametrisation of a triple-satellite model feasible. To address slow- and fast variations for multi-satellite reception, the fading interdependence between synchronously received satellite signals is analysed from high-resolution measurement data. Hence additional correlations besides the state correlation are identified and firstly considered in the new multi-satellite LMS model. The modelling results are compared with the measurements in terms of first- and second order statistics, where improvements in describing diversity become visible when compared to existing models. Model parameters are derived from large-scale measurement campaigns to enable a time series generation for different environments and various constellations of satellites. The applicability of the new model is finally demonstrated by comparing the performance of different satellite constellations with diversity

On Channel Modelling for Land Mobile Satellite Reception

In modernen Satellitenrundfunksystemen werden Methoden wie Zeitdiversität (Empfang von zeitlich verteilter Information) und Winkeldiversität (Empfang von mehreren Satelliten mit unterschiedlichen Positionen) angewandt, um die geforderte Dienstequalität für den mobilen Empfang zu gewährleisten. Zur Untersuchung der Ausbreitungseffekte des landmobilen Satellitenkanals sowohl der Wirksamkeit von Diversität werden statistische Modelle benötigt, die den zeitlichen Signalschwund des Empfangssignals nachbilden. In der vorliegenden Arbeit wird ein Kanalmodell für den Mehrsatellitenempfang entwickelt, welches genaue Versorgungsvorhersagen mit Zeit- und Winkeldiversität erlaubt.Grundlage ist ein Einsatellitenmodell, welches großräumige Schwankungen im Kanal durchdie Zustände ’gut’ und ’schlecht’ definiert, und den langsamen und schnellen Signalschwund gemäß einer variablen Loo-Verteilung beschreibt, deren Parameter nach jedem Zustandswechsel stochastisch bestimmt werden. Für die Zustandsmodellierung mit zwei Satelliten wird ein 'semi-Markov Modell für korrellierte Zustandssequenzen' erarbeitet. Damit können, unter Berücksichtigung der Statistiken der Einzelkanäle und deren Korrelationskoeffizient, die Zustandswahrscheinlichkeiten und-längen exakt simuliert werden. Für die Zustandsmodellierung mit drei Satelliten wird ein 'Master-Slave'-Ansatz entwickelt. Dabei sind die Zustandssequenzen zweier ’Slaves’ bedingt abhängig zur ’Master’-Sequenz. Der ’Master-Slave’-Ansatz ermöglicht die Parametrisierung eines Dreisatellitenmodells. Zur Beschreibung des langsamen und schnellen Signalschwunds im Mehrsatellitenkanal wirddie Wechselbeziehung zwischen synchron gemessenen Satellitensignalen näher untersucht.Es stellt sich heraus dass weitere Signalkorrelationen berücksichtigt werden sollten, dieerstmalig im neuen LMS-Kanalmodell implementiert werden. Die Simulationssergebnisse werden in Statistiken erster und zweiter Ordnung den Messdaten gegenübergestellt. Im Vergleich zu bestehenden Modellen werden Verbesserungen nach Berücksichtigung von Diversität deutlich. Die Parameter für das Mehrsatellitenkanalmodell wurden aus umfassenden Messkampagnen abgeleitet und gewährleisten die Signalsimulation für verschiedene Umgebungen und Satellitenpositionen. Abschließend wird das Kanalmodell für einen ersten Vergleich verschiedener Satellitenkonfigurationen mit Zeit- und Winkeldiversität angewandt.Modern digital satellite broadcasting systems combine time diversity (i.e. information is spread over a certain time interval) with angle diversity (i.e. information is received from multiple satellites in different orbital positions) to ensure uninterrupted service for mobile receivers over large areas. For assessing propagation effects of the land mobile satellite (LMS) channel and to study the efficacy of diversity, statistical models are required which generate time series of the received fading signal. In this thesis a narrowband LMS channel model for multi-satellite reception is developed focusing on accurate coverage prediction under consideration of angle- and time diversity. Basis is an existing single-satellite model, which describes large-scale signal variations of the channel by 'good' and 'bad' states, and models slow- and fast signal variations according to a versatile Loo distribution, which parameters are selected randomly when the channel enters a new state. For dual-satellite state modelling, a semi-Markov model for correlated state sequences is developed. It provides an accurate state probability and state duration modelling by considering the statistics of separate channels and their correlation coefficient. For the state modelling with three satellites, a new Master-Slave concept is introduced. Therefore, state sequences of slave satellites are conditioned by an existing master state sequence. The great advantage is that Master-Slave makes the parametrisation of a triple-satellite model feasible. To address slow- and fast variations for multi-satellite reception, the fading interdependence between synchronously received satellite signals is analysed from high-resolution measurement data. Hence additional correlations besides the state correlation are identified and firstly considered in the new multi-satellite LMS model. The modelling results are compared with the measurements in terms of first- and second order statistics, where improvements in describing diversity become visible when compared to existing models. Model parameters are derived from large-scale measurement campaigns to enable a time series generation for different environments and various constellations of satellites. The applicability of the new model is finally demonstrated by comparing the performance of different satellite constellations with diversity