Comparative Analysis of Non Linear Estimation Schemes used for Undersea Sonar Applications

    The performance evaluation of various passive underwater target tracking algorithms like Pseudo Linear Estimator, Maximum Likelihood Estimator, Modified Gain Bearings-only Extended Kalman Filter, Unscented Kalman Filter, Parameterized Modified Gain Bearings-only Extended Kalman Filter and Particle Filter coupled with Modified Gain Bearings-only Extended Kalman Filter using bearings-only measurements is carried out with various scenarios in Monte Carlo Simulation. The performance of Parameterized Modified Gain Bearings-only Extended Kalman Filter is found to be better than all estimates

Multistatic Tracking with the Maximum Likelihood Probabilistic Multi-Hypothesis Tracker

Multistatic sonar tracking is a difficult proposition. The ocean environment typically features very complex propagation conditions, causing low target probabilities of detection and high clutter levels. Additionally, most sonar targets are relatively low speed, which makes it difficult to use Doppler (if available) to separate target returns from clutter returns. The Maximum Likelihood Probabilistic Data Association Tracker (ML-PDA) and the Maximum Likelihood Probabilistic Multi-Hypothesis Tracker (ML-PMHT) --- a similar algorithm to ML-PDA --- can be implemented as effective multistatic trackers. This dissertation will develop a tracking framework for these algorithms. This framework will focus mainly on ML-PMHT, which has an inherent advantage in that its log-likelihood ratio (LLR) has a simple multitarget formulation, which allows it to be implemented as a true multitarget tracker. First, this multitarget LLR will be implemented for ML-PMHT, which will give it superior performance over ML-PDA for instances where multiple targets are closely spaced with similar motion dynamics. Next, the performance of ML-PMHT will be compared when it is applied in Cartesian measurement space and in delay-bearing measurement space, where the measurement covariance is more accurately represented. Following this, a maneuver-model parameterization will be introduced that will allow ML-PDA and ML-PMHT to follow sharply maneuvering targets; their previous straight-line parameterization only allowed them to follow moderately maneuvering targets. Finally, a novel method of determining a tracking threshold for ML-PMHT will be developed by applying extreme value theory to the probabilistic properties of the clutter. This will also be done with target measurements, which will allow the issue of trackability for ML-PMHT to be explored. Probabilistic expressions for the maximum values of the LLR surface caused by both clutter and the target will be developed, which will allow for the determination of target trackability in any given scenario

ParallaxBA: Bundle adjustment using parallax angle feature parametrization

©The Author(s) 2015. The main contribution of this paper is a novel feature parametrization based on parallax angles for bundle adjustment (BA) in structure and motion estimation from monocular images. It is demonstrated that under certain conditions, describing feature locations using their Euclidean XYZ coordinates or using inverse depth in BA leads to ill-conditioned normal equations as well as objective functions that have very small gradients with respect to some of the parameters describing feature locations. The proposed parallax angle feature parametrization in BA (ParallaxBA) avoids both of the above problems leading to better convergence properties and more accurate motion and structure estimates. Simulation and experimental datasets are used to demonstrate the impact of different feature parametrizations on BA, and the improved convergence, efficiency and accuracy of the proposed ParallaxBA algorithm when compared with some existing BA packages such as SBA, sSBA and g2o. The C/C++ source code of ParallaxBA is available on OpenSLAM (https://openslam.org/)

A multi-hypothesis approach for range-only simultaneous localization and mapping with aerial robots

Los sistemas de Range-only SLAM (o RO-SLAM) tienen como objetivo la construcción de un mapa formado por la posición de un conjunto de sensores de distancia y la localización simultánea del robot con respecto a dicho mapa, utilizando únicamente para ello medidas de distancia. Los sensores de distancia son dispositivos capaces de medir la distancia relativa entre cada par de dispositivos. Estos sensores son especialmente interesantes para su applicación a vehículos aéreos debido a su reducido tamaño y peso. Además, estos dispositivos son capaces de operar en interiores o zonas con carencia de señal GPS y no requieren de una línea de visión directa entre cada par de dispositivos a diferencia de otros sensores como cámaras o sensores laser, permitiendo así obtener una lectura de datos continuada sin oclusiones. Sin embargo, estos sensores presentan un modelo de observación no lineal con una deficiencia de rango debido a la carencia de información de orientación relativa entre cada par de sensores. Además, cuando se incrementa la dimensionalidad del problema de 2D a 3D para su aplicación a vehículos aéreos, el número de variables ocultas del modelo aumenta haciendo el problema más costoso computacionalmente especialmente ante implementaciones multi-hipótesis. Esta tesis estudia y propone diferentes métodos que permitan la aplicación eficiente de estos sistemas RO-SLAM con vehículos terrestres o aéreos en entornos reales. Para ello se estudia la escalabilidad del sistema en relación al número de variables ocultas y el número de dispositivos a posicionar en el mapa. A diferencia de otros métodos descritos en la literatura de RO-SLAM, los algoritmos propuestos en esta tesis tienen en cuenta las correlaciones existentes entre cada par de dispositivos especialmente para la integración de medidas estÃa˛ticas entre pares de sensores del mapa. Además, esta tesis estudia el ruido y las medidas espúreas que puedan generar los sensores de distancia para mejorar la robustez de los algoritmos propuestos con técnicas de detección y filtración. También se proponen métodos de integración de medidas de otros sensores como cámaras, altímetros o GPS para refinar las estimaciones realizadas por el sistema RO-SLAM. Otros capítulos estudian y proponen técnicas para la integración de los algoritmos RO-SLAM presentados a sistemas con múltiples robots, así como el uso de técnicas de percepción activa que permitan reducir la incertidumbre del sistema ante trayectorias con carencia de trilateración entre el robot y los sensores de destancia estáticos del mapa. Todos los métodos propuestos han sido validados mediante simulaciones y experimentos con sistemas reales detallados en esta tesis. Además, todos los sistemas software implementados, así como los conjuntos de datos registrados durante la experimentación han sido publicados y documentados para su uso en la comunidad científica

Evaluation of 2 1-D cloud models for the analysis of VAS soundings

Evaluation of the satellite Visual Infrared Spin Scan Radiometer Atmospheric Sounder (VISSR) has begun to document several of its critical shortcomings as far as numerical cloud models are concerned: excessive smoothing of thermal inversions; imprecise measurement of boundary layer moisture; and tendency to exaggerate atmospheric stability. The sensitivity of 1-D cloud models to their required inputs is stressed with special attention to those parameters obtained from atmospheric soundings taken by the VAS or rawinsonde. In addition to performing model experiments using temperature and moisture profiles having the general characteristics of VAS soundings, standard input sensitivity tests were made and 1-D model performance was compared with observations and the results of a 2-D model experiment using AVE/VAS data (Atmospheric Variability Experiment). Although very encouraging, the results are not sufficient to make any specific conclusions. In general, the VAS soundings are likely to be inadequate to provide the cloud base (and subcloud layer) information needed for inputs to current cumulus models. Above cloud base, the tendency to exaggerate the stability of the atmosphere requires solution before meaningful model experiments are run

Impact of multiple radar reflectivity data assimilation on the numerical simulation of a flash flood event during the HyMeX campaign

An analysis to evaluate the impact of multiple radar reflectivity data with a three-dimensional variational (3-D-Var) assimilation system on a heavy precipitation event is presented. The main goal is to build a regionally tuned numerical prediction model and a decision-support system for environmental civil protection services and demonstrate it in the central Italian regions, distinguishing which type of observations, conventional and not (or a combination of them), is more effective in improving the accuracy of the forecasted rainfall. In that respect, during the first special observation period (SOP1) of HyMeX (Hydrological cycle in the Mediterranean Experiment) campaign several intensive observing periods (IOPs) were launched and nine of which occurred in Italy. Among them, IOP4 is chosen for this study because of its low predictability regarding the exact location and amount of precipitation. This event hit central Italy on 14 September 2012 producing heavy precipitation and causing several cases of damage to buildings, infrastructure, and roads. Reflectivity data taken from three C-band Doppler radars running operationally during the event are assimilated using the 3-D-Var technique to improve high-resolution initial conditions. In order to evaluate the impact of the assimilation procedure at different horizontal resolutions and to assess the impact of assimilating reflectivity data from multiple radars, several experiments using the Weather Research and Forecasting (WRF) model are performed. Finally, traditional verification scores such as accuracy, equitable threat score, false alarm ratio, and frequency bias - interpreted by analysing their uncertainty through bootstrap confidence intervals (CIs) - are used to objectively compare the experiments, using rain gauge data as a benchmark

Design methodology addressing static/reconfigurable partitioning optimizing software defined radio (SDR) implementation through FPGA dynamic partial reconfiguration and rapid prototyping tools

The characteristics people request for communication devices become more and more demanding every day. And not only in those aspects dealing with communication speed, but also in such different characteristics as different communication standards compatibility, battery life, device size or price. Moreover, when this communication need is addressed by the industrial world, new characteristics such as reliability, robustness or time-to-market appear. In this context, Software Defined Radios (SDR) and evolutions such as Cognitive Radios or Intelligent Radios seem to be the technological answer that will satisfy all these requirements in a short and mid-term. Consequently, this PhD dissertation deals with the implementation of this type of communication system. Taking into account that there is no limitation neither in the implementation architecture nor in the target device, a novel framework for SDR implementation is proposed. This framework is made up of FPGAs, using dynamic partial reconfiguration, as target device and rapid prototyping tools as designing tool. Despite the benefits that this framework generates, there are also certain drawbacks that need to be analyzed and minimized to the extent possible. On this purpose, a SDR design methodology has been designed and tested. This methodology addresses the static/reconfigurable partitioning of the SDRs in order to optimize their implementation in the aforementioned framework. In order to verify the feasibility of both the design framework and the design methodology, several implementations have been carried out making use of them. A multi-standard modulator implementing WiFi, WiMAX and UMTS, a small-form-factor cognitive video transmission system and the implementation of several data coding functions over R3TOS, a hardware operating system developed by the University of Edinburgh, are these implementations.Las características que la gente exige a los dispositivos de comunicaciones son cada día más exigentes. Y no solo en los aspectos relacionados con la velocidad de comunicación, sino que también en diferentes características como la compatibilidad con diferentes estándares de comunicación, autonomía, tamaño o precio. Es más, cuando esta necesidad de comunicación se traslada al mundo industrial, aparecen nuevas características como fiabilidad, robustez o plazo de comercialización que también es necesario cubrir. En este contexto, las Radios Definidas por Software (SDR) y evoluciones como las Radios Cognitivas o Radios Inteligentes parecen la respuesta tecnológica que va a satisfacer estas necesidades a corto y medio plazo. Por ello, esta tesis doctoral aborda la implementación de este tipo de sistemas de comunicaciones. Teniendo en cuenta que no existe una limitación, ni en la arquitectura de implementación, ni en el tipo de dispositivo a usar, se propone un nuevo entrono de diseño formado por las FPGAs, haciendo uso de la reconfiguración parcial dinámica, y por las herramientas de prototipado rápido. A pesar de que este entorno de diseño ofrece varios beneficios, también genera algunos inconvenientes que es necesario analizar y minimizar en la medida de lo posible. Con este objetivo, se ha diseñado y verificado una metodología de diseño de SDRs. Esta metodología se encarga del particionado estático/reconfigurable de las SDRs para optimizar su implementación sobre el entrono de diseño antes comentado. Para verificar la viabilidad tanto del entorno, como de la metodología de diseño propuesta, se han realizado varias implementaciones que hacen uso de ambas cosas. Estas implementaciones son: un modulador multi-estándar que implementa WiFi, WiMAX y UMTS, un sistema cognitivo y compacto de transmisión de video y la implementación de varias funciones de codificación de datos sobre R3TOS, un sistema operativo hardware desarrollado por la Universidad de Edimburgo

The PHOENIX Exoplanet Retrieval Algorithm and Using H−^{-} Opacity as a Probe in Ultra-hot Jupiters

Atmospheric retrievals are now a standard tool to analyze observations of exoplanet atmospheres. This data-driven approach quantitatively compares atmospheric models to observations in order to estimate atmospheric properties and their uncertainties. In this paper, we introduce a new retrieval package, the PHOENIX Exoplanet Retrieval Analysis (PETRA). PETRA places the PHOENIX atmosphere model in a retrieval framework, allowing us to combine the strengths of a well-tested and widely-used atmosphere model with the advantages of retrieval algorithms. We validate PETRA by retrieving on simulated data for which the true atmospheric state is known. We also show that PETRA can successfully reproduce results from previously published retrievals of WASP-43b and HD 209458b. For the WASP-43b results, we show the effect that different line lists and line profile treatments have on the retrieved atmospheric properties. Lastly, we describe a novel technique for retrieving the temperature structure and $e^{-}$ density in ultra-hot Jupiters using H$^{-}$ opacity, allowing us to probe atmospheres devoid of most molecular features with JWST.Comment: 17 pages, 18 figures. Accepted for publication in A