Multi-Sensor Data Fusion for Travel Time Estimation

The importance of travel time estimation has increased due to the central role it plays in a number of emerging intelligent transport systems and services including Advanced Traveller Information Systems (ATIS), Urban Traffic Control (UTC), Dynamic Route Guidance (DRG), Active Traffic Management (ATM), and network performance monitoring. Along with the emerging of new sensor technologies, the much greater volumes of near real time data provided by these new sensor systems create opportunities for significant improvement in travel time estimation. Data fusion as a recent technique leads to a promising solution to this problem. This thesis presents the development and testing of new methods of multi-sensor data fusion for the accurate, reliable and robust estimation of travel time. This thesis reviews the state-of-art data fusion approaches and its application in transport domain, and discusses both of opportunities and challenging of applying data fusion into travel time estimation in a heterogeneous real time data environment. For a particular England highway scenario where ILDs and ANPR data are largely available, a simple but practical fusion method is proposed to estimate the travel time based on a novel relationship between space-mean-speed and time-mean-speed. In developing a general fusion framework which is able to fuse ILDs, GPS and ANPR data, the Kalman filter is identified as the most appropriate fundamental fusion technique upon which to construct the required framework. This is based both on the ability of the Kalman filter to flexibly accommodate well-established traffic flow models which describe the internal physical relation between the observed variables and objective estimates and on its ability to integrate and propagate in a consistent fashion the uncertainty associated with different data sources. Although the standard linear Kalman filter has been used for multi-sensor travel time estimation in the previous research, the novelty of this research is to develop a nonlinear Kalman filter (EKF and UKF) fusion framework which improves the estimation performance over those methods based on the linear Kalman filter. This proposed framework is validated by both of simulation and real-world scenarios, and is demonstrated the effectiveness of estimating travel time by fusing multi-sensor sources

Simplified multitarget tracking using the PHD filter for microscopic video data

The probability hypothesis density (PHD) filter from the theory of random finite sets is a well-known method for multitarget tracking. We present the Gaussian mixture (GM) and improved sequential Monte Carlo implementations of the PHD filter for visual tracking. These implementations are shown to provide advantages over previous PHD filter implementations on visual data by removing complications such as clustering and data association and also having beneficial computational characteristics. The GM-PHD filter is deployed on microscopic visual data to extract trajectories of free-swimming bacteria in order to analyze their motion. Using this method, a significantly larger number of tracks are obtained than was previously possible. This permits calculation of reliable distributions for parameters of bacterial motion. The PHD filter output was tested by checking agreement with a careful manual analysis. A comparison between the PHD filter and alternative tracking methods was carried out using simulated data, demonstrating superior performance by the PHD filter in a range of realistic scenarios

Nested filtering methods for Bayesian inference in state space models

Mención Internacional en el título de doctorA common feature to many problems in some of the most active fields of science is the need to calibrate (i.e., estimate the parameters) and then forecast the time evolution of high-dimensional dynamical systems using sequentially collected data. In this dissertation we introduce a generalised nested filtering methodology that is structured in (two or more) intertwined layers in order to estimate the static parameters and the dynamic state variables of nonlinear dynamical systems. This methodology is essentially probabilistic. It aims at recursively computing the sequence of posterior probability distributions of the unknown model parameters and its (time-varying) state variables conditional on the available observations. To be specific, in the first layer of the filter we approximate the posterior probability distribution of the static parameters and in the consecutive layers we employ filtering (or data assimilation) techniques to track and predict different conditional probability distributions of the state variables. We have investigated the use of different Monte Carlo-based methods and Gaussian filtering techniques in each of the layers, leading to a wealth of algorithms. In a first approach, we have introduced a nested filtering methodology of two layers that aims at recursively estimating the static parameters and the dynamical state variables of a state space model. This probabilistic scheme uses Monte Carlo-based methods in the first layer of the filter, combined with the use of Gaussian filters in the second layer. Different from the nested particle filter (NPF) of [25], the use of Gaussian filtering techniques in the second layer allows for fast implementations, leading to algorithms that are better suited to high-dimensional systems. As each layer uses different types of methods, we refer to the proposed methodology as nested hybrid filtering. We specifically explore the combination of Monte Carlo and quasi–Monte Carlo approximations in the first layer, including sequential Monte Carlo (SMC) and sequential quasi-Monte Carlo (SQMC), with standard Gaussian filtering methods in the second layer, such as the ensemble Kalman filter (EnKF) and the extended Kalman filter (EKF). However, other algorithms can fit naturally within the framework. Additionally, we prove a general convergence result for a class of procedures that use SMC in the first layer and we show numerical results for a stochastic two-scale Lorenz 96 system, a model commonly used to assess data assimilation (filtering) procedures in Geophysics. We apply and compare different implementations of the methodology to the tracking of the state and the estimation of the fixed parameters. We show estimation and forecasting results, obtained with a desktop computer, for up to 5000 dynamic state variables. As an extension of the nested hybrid filtering methodology, we have introduced a class of schemes that can incorporate deterministic sampling techniques (such as the cubature Kalman filter (CKF) or the unscented Kalman filter (UKF)) in the first layer of the algorithm, instead of the Monte Carlo-based methods employed in the original procedure. As all the methods used in this scheme are Gaussian, we refer to this class of algorithms as nested Gaussian filters. One more time, we reduce the computational cost with the proposed scheme, making the resulting algorithms potentially better-suited for high-dimensional state and parameter spaces. In the numerical results, we describe and implement a specific instance of the new method (a UKF-EKF algorithm) and evaluate its average performance in terms of estimation errors and running times for nonlinear stochastic models. Specifically, we present numerical results for a stochastic Lorenz 63 model using synthetic data, as well as for a stochastic volatility model with real-world data. Finally, we have extended the proposed methodology in order to estimate the static parameters and the dynamical variables of a class of heterogeneous multi-scale state-space models [1]. This scheme combines three or more layers of filters, one inside the other. Each of the layers corresponds to the different time scales that are relevant to the dynamics of this kind of state-space models, allocating the variables with the greatest time scales (the slowest ones) in the outer-most layer and the variables with the smallest time scales (the fastest ones) to the inner-most layer. In particular, we describe a three-layer filter that approximates the posterior probability distribution of the parameters in a first layer of computation, in a second layer we approximate the posterior probability distribution of the slow state variables, and the posterior probability distribution of the fast state variables is approximated in a third layer. To be specific, we describe two possible algorithms that derive from this scheme, combining Monte Carlo methods and Gaussian filters at different layers. The first method uses SMC methods in both first and second layers, together with a bank of UKFs in the third layer (i.e., a SMC-SMC-UKF algorithm). The second method employs a SMC in the first layer, EnKFs at the second layer and introduces the use of a bank of EKFs in the third layer (i.e., a SMC-EnKF-EKF algorithm). We present numerical results for a two-scale stochastic Lorenz 96 model with synthetic data.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Víctor Elvira Arregui.- Secretario: Stefano Cabras.- Vocal: David Luengo Garcí

Implementation in Embedded Systems of State Observers Based on Multibody Dynamics

Programa Oficial de Doutoramento en Enxeñaría Naval e Industrial . 5015V01[Abstract] Simulation has become an important tool in the industry that minimizes either the cost and time of new products development and testing. In the automotive industry, the use of simulation is being extended to virtual sensing. Through an accurate model of the vehicle combined with a state estimator, variables that are difficult or costly to measure can be estimated. The virtual sensing approach is limited by the low computational power of invehicle hardware due to the strictest timing, reliability and safety requirements imposed by automotive standards. With the new generation hardware, the computational power of embedded platforms has increased. They are based on heterogeneous processors, where the main processor is combined with a co-processor, such as Field Programmable Gate Arrays (FPGAs). This thesis explores the implementation of a state estimator based on a multibody model of a vehicle in new generation embedded hardware. Different implementation strategies are tested in order to explore the advantages that an FPGA can provide. A new state-parameter-input observer is developed, providing accurate estimations. The proposed observer is combined with an efficient multibody model of a vehicle, achieving real-time execution.[Resumen] La simulación se ha convertido en una importante herramienta para la industria que permite minimizar tanto costes como tiempo de desarrollo y test de nuevos productos. En automoción, el uso de la simulación se extiende al desarrollo de sensores virtuales. Mediante un modelo preciso de un vehículo combinado con un observador de estados, variables que son caras o imposibles de medir pueden ser estimadas. La principal limitación para utilizar sensores virtuales en los vehículos es la baja potencia computacional de los procesadores instalados a bordo, debido a los estrictos requisitos impuestos por los standards de automoción. Con el hardware de nueva generación, el poder de cálculo de las plataformas empotradas se ha visto incrementado. Estos nuevos procesadores son del tipo heterogéneo, donde el procesador principal se complementa con un co-procesador, como una Field Programmable Gate Array (FPGA). Esta tesis explora la implementación de un observador de estados basado en un modelo multicuerpo de un vehículo en hardware empotrado de nueva generación. Se han probado diferentes implementaciones para evaluar las ventajas de disponer de una FPGA en el procesador. Se ha desarrollado un nuevo observador de estados, parámetros y entradas que permite obtener estimaciones de gran precisión. Combinando dicho observador con un eficiente modelo multicuerpo de un vehículo, se consigue rendimiento en tiempo real.[Resumo] A simulación estase a converter nunha importante ferramenta na industria que permite minimizar custes e tempo tanto de desenvolvemento coma de test de novos productos. En automoción, o uso da simulación esténdese á implementación de sensores virtuais. Mediante un modelo preciso dun vehículo combinado cun observador de estados, pódense estimar variables que son caras ou imposíbeis de medir. A principal limitación para utilizar sensores virtuais nos vehículos é a baixa potencia computacional dos procesadores instalados a bordo, debido aos estritos requisitos impostos polos estándares de automoción. Co hardware de nova xeración, o poder de cálculo das plataformas empotradas vese incrementado. Estos novos procesadores son de tipo heteroxéneo, onde o procesador principal compleméntase cun co-procesador, coma unha Field Programmable Gate Array (FPGA). Esta tese explora a implementación dun observador de estados basado nun modelo multicorpo dun vehículo en hardware empotrado de nova xeración. Diferentes implementacións foron probadas para avaliar as vantaxes de dispoñer dunha FPGA no procesador. Un novo observador de estados, parámetros e entradas deseñado nesta tese permite obter estimacións de gran precisión. Combinando dito observador cun eficiente modelo multicorpo dun vehículo, conséguese rendemento de tempo real

Optimal Collision Avoidance Trajectories for Unmanned/Remotely Piloted Aircraft

The post-911 environment has punctuated the force-multiplying capabilities that Remotely Piloted Aircraft (RPA) provides combatant commanders at all echelons on the battlefield. Not only have unmanned aircraft systems made near-revolutionary impacts on the battlefield, their utility and proliferation in law enforcement, homeland security, humanitarian operations, and commercial applications have likewise increased at a rapid rate. As such, under the Federal Aviation Administration (FAA) Modernization and Reform Act of 2012, the United States Congress tasked the FAA to provide for the safe integration of civil unmanned aircraft systems into the national airspace system (NAS) as soon as practicable, but not later than September 30, 2015. However, a necessary entrance criterion to operate RPAs in the NAS is the ability to Sense and Avoid (SAA) both cooperative and noncooperative air traffic to attain a target level of safety as a traditional manned aircraft platform. The goal of this research effort is twofold: First, develop techniques for calculating optimal avoidance trajectories, and second, develop techniques for estimating an intruder aircraft\u27s trajectory in a stochastic environment. This dissertation describes the optimal control problem associated with SAA and uses a direct orthogonal collocation method to solve this problem and then analyzes these results for different collision avoidance scenarios

Tightly Integrating Optical and Inertial Sensors for Navigation Using the UKF

The motivation of this research is to address the benefits of tightly integrating optical and inertial sensors where GNSS signals are not available. The research begins with describing the navigation problem. Then, error and measurement models are presented. Given a set of features, a feature detection and projection algorithm is developed which utilizes inertial measurements to predict vectors in the feature space between images. The unscented Kalman filter is applied to the navigation system using the inertial measurements and feature matches to estimate the navigation trajectory. Finally, the image-aided navigation algorithm is tested using a simulation and an experiment. As a result, the optical measurements combined with the inertial sensors result in improved performance for non-GNSS based navigation

Kalman Filtering With State Constraints: A Survey of Linear and Nonlinear Algorithms

The Kalman filter is the minimum-variance state estimator for linear dynamic systems with Gaussian noise. Even if the noise is non-Gaussian, the Kalman filter is the best linear estimator. For nonlinear systems it is not possible, in general, to derive the optimal state estimator in closed form, but various modifications of the Kalman filter can be used to estimate the state. These modifications include the extended Kalman filter, the unscented Kalman filter, and the particle filter. Although the Kalman filter and its modifications are powerful tools for state estimation, we might have information about a system that the Kalman filter does not incorporate. For example, we may know that the states satisfy equality or inequality constraints. In this case we can modify the Kalman filter to exploit this additional information and get better filtering performance than the Kalman filter provides. This paper provides an overview of various ways to incorporate state constraints in the Kalman filter and its nonlinear modifications. If both the system and state constraints are linear, then all of these different approaches result in the same state estimate, which is the optimal constrained linear state estimate. If either the system or constraints are nonlinear, then constrained filtering is, in general, not optimal, and different approaches give different results

Sonar attentive underwater navigation in structured environment

One of the fundamental requirements of a persistently Autonomous Underwater Vehicle (AUV) is a robust navigation system. The success of most complex robotic tasks depends on the accuracy of a vehicle’s navigation system. In a basic form, an AUV estimates its position using an on-board navigation sensors through Dead-Reckoning (DR). However DR navigation systems tends to drift in the long run due to accumulated measurement errors. One way of mitigating this problem require the use of Simultaneous Localization and Mapping (SLAM) by concurrently mapping external environment features. The performance of a SLAM navigation system depends on the availability of enough good features in the environment. On the contrary, a typical underwater structured environment (harbour, pier or oilfield) has a limited amount of sonar features in a limited locations, hence exploitation of good features is a key for effective underwater SLAM. This thesis develops a novel attentive sonar line feature based SLAM framework that improves the performance of a SLAM navigation by steering a multibeam sonar sensor,which is mounted on a pan and tilt unit, towards feature-rich regions of the environment. A sonar salience map is generated at each vehicle pose to identify highly informative and stable regions of the environment. Results from a simulated test and real AUV experiment show an attentive SLAM performs better than a passive counterpart by repeatedly visiting good sonar landmarks