<p> Detection and estimation of multiple unresolved targets with a monopulse radar is a challenging problem. For ideal single bin processing, it was shown in the literature that at most two unresolved targets can be extracted from the complex matched filter output signal. In this thesis, a new algorithm is developed to jointly detect and track more than two targets from a single detection. This method involves the use of tracking data in the detection process. For this purpose, target states are transformed into the detection parameter space, which involves high nonlinearity. In order to handle this, the sequential Monte Carlo (SMC) method, which has proven to be effective in nonlinear non-Gaussian estimation problems, is used as the basis of the closed loop system for tracking multiple unresolved targets. In addition to the standard SMC steps, the detection parameters corresponding to the predicted particles are evaluated using the nonlinear monopulse radar beam model. This in turn enables the evaluation of the likelihood of the monopulse signal given tracking data. Hypothesis testing is then used to find the correct detection event. The particles are updated and resampled according to the hypothesis that has the highest likelihood (score). A simulated amplitude comparison monopulse radar is used to generate the data and to validate the extraction and tracking of more than two unresolved targets.</p> / Thesis / Master of Applied Science (MASc)

Accurate hydrologic forecasting is vital for proper water resource management. Practices that are impacted by these forecasts include power generation, reservoir management, agricultural water use, and flood early warning systems. Despite these needs, the models largely used are simplifications of the real world and are therefore imperfect. The forecasters face other challenges in addition to the model uncertainty, which includes imperfect observations used for model calibration and validation, imperfect meteorological forecasts, and the ability to effectively communicate forecast results to decision-makers. Bayesian methods are commonly used to address some of these issues, and this thesis will be focused on improving methods related to recursive Bayesian estimation, more commonly known as data assimilation. Data assimilation is a means to optimally account for the uncertainties in observations, models, and forcing data. In the literature, data assimilation for urban hydrologic and flood forecasting is rare; therefore the main areas of study in this thesis are urban and semi-urban watersheds. By providing improvements to data assimilation methods, both hydrologic and flood forecasting can be enhanced in these areas. This work explored the use of alternative data products as a type of observation that can be assimilated to improve hydrologic forecasting in an urban watershed. The impact of impervious surfaces in urban and semi-urban watersheds was also evaluated in regards to its impact on remotely sensed soil moisture assimilation. Lack of observations is another issue when it comes to data assimilation, particularly in semi- or fully-distributed models; because of this, an improved method for updating locations which do not have observations was developed which utilizes information theory’s mutual information. Finally, we explored extending data assimilation into the short-term forecast by using prior knowledge of how a model will respond to forecasted forcing data. Results from this work found that using alternative data products such as those from the Snow Data Assimilation System or the Soil Moisture and Ocean Salinity mission, can be effective at improving hydrologic forecasting in urban watersheds. They also were effective at identifying a limiting imperviousness threshold for soil moisture assimilation into urban and semi-urban watersheds. Additionally, the inclusion of mutual information between gauged and ungauged locations in a semi-distributed hydrologic model was able to provide better state updates in models. Finally, by extending data assimilation into the short-term forecast, the reliability of the forecasts could be improved substantially. / Dissertation / Doctor of Philosophy (PhD) / The ability to accurately model hydrological systems is essential, as that allows for better planning and decision making in water resources management. The better we can forecast the hydrologic response to rain and snowmelt events, the better we can plan and manage our water resources. This includes better planning and usage of water for agricultural purposes, better planning and management of reservoirs for power generation, and better preparing for flood events. Unfortunately, hydrologic models primarily used are simplifications of the real world and are therefore imperfect. Additionally, our measurements of the physical system responses to atmospheric forcing can be prone to both systematic and random errors that need to be accounted for. To address these limitations, data assimilation can be used to improve hydrologic forecasts by optimally accounting for both model and observation uncertainties. The work in this thesis helps to further advance and improve data assimilation, with a focus on enhancing hydrologic forecasting in urban and semi-urban watersheds. The research presented herein can be used to provide better forecasts, which allow for better planning and decision making.

The focus of this thesis is to implement factor graphs into the problem of detection, classification, and localization (DCL) of underwater objects using active SOund Navigation And Ranging (SONAR). A factor graph is a bipartite graphical representation of the decomposition of a particular function. Messages are passed along the edges connecting factor and variable nodes, on which, a message passing algorithm is applied to compute the posterior probabilities at a particular node. This thesis addresses two issues. In the first section, the formulation of factor graphs for each section of the DCL chain required followed by their closed-form solutions. For the detector, the factor graph determines if the signal is a detection or simply noise. In the classifier, it outputs the probability for the elements in the class. Last, when using a factor graph for the tracker, it gives the estimated state of the object being tracked. The second part concentrates on the application to Continuous Active SONAR (CAS). When using CAS, a bistatic configuration is used allowing for a more rapid update rate where two unmanned underwater vehicles (UUVs) are used as the receiver and transmitter. The goal is to evaluate CAS's effectiveness to determine if the tracking accuracy improves as the transmit interval decreases. If CAS proves to be more efficient in target tracking, the next objective is to determine which messages sent between the two UUVs are most beneficial. To test this, a particle filter simulation is used. / Master of Science

In this thesis, we consider some popular stochastic differential equation models used in finance, such as the Vasicek Interest Rate model, the Heston model and a new fractional Heston model. We discuss how to perform inference about unknown quantities associated with these models in the Bayesian framework. We describe sequential importance sampling, the particle filter and the auxiliary particle filter. We apply these inference methods to the Vasicek Interest Rate model and the standard stochastic volatility model, both to sample from the posterior distribution of the underlying processes and to update the posterior distribution of the parameters sequentially, as data arrive over time. We discuss the sensitivity of our results to prior assumptions. We then consider the use of Markov chain Monte Carlo (MCMC) methodology to sample from the posterior distribution of the underlying volatility process and of the unknown model parameters in the Heston model. The particle filter and the auxiliary particle filter are also employed to perform sequential inference. Next we extend the Heston model to the fractional Heston model, by replacing the Brownian motions that drive the underlying stochastic differential equations by fractional Brownian motions, so allowing a richer dependence structure across time. Again, we use a variety of methods to perform inference. We apply our methodology to simulated and real financial data with success. We then discuss how to make forecasts using both the Heston and the fractional Heston model. We make comparisons between the models and show that using our new fractional Heston model can lead to improve forecasts for real financial data.

This thesis deals with estimation of states and parameters in nonlinear and non-Gaussian dynamic systems. Sequential Monte Carlo methods are mainly used to this end. These methods rely on models of the underlying system, motivating some developments of the model concept. One of the main reasons for the interest in nonlinear estimation is that problems of this kind arise naturally in many important applications. Several applications of nonlinear estimation are studied. The models most commonly used for estimation are based on stochastic difference equations, referred to as state-space models. This thesis is mainly concerned with models of this kind. However, there will be a brief digression from this, in the treatment of the mathematically more intricate differential-algebraic equations. Here, the purpose is to write these equations in a form suitable for statistical signal processing. The nonlinear state estimation problem is addressed using sequential Monte Carlo methods, commonly referred to as particle methods. When there is a linear sub-structure inherent in the underlying model, this can be exploited by the powerful combination of the particle filter and the Kalman filter, presented by the marginalized particle filter. This algorithm is also known as the Rao-Blackwellized particle filter and it is thoroughly derived and explained in conjunction with a rather general class of mixed linear/nonlinear state-space models. Models of this type are often used in studying positioning and target tracking applications. This is illustrated using several examples from the automotive and the aircraft industry. Furthermore, the computational complexity of the marginalized particle filter is analyzed. The parameter estimation problem is addressed for a relatively general class of mixed linear/nonlinear state-space models. The expectation maximization algorithm is used to calculate parameter estimates from batch data. In devising this algorithm, the need to solve a nonlinear smoothing problem arises, which is handled using a particle smoother. The use of the marginalized particle filter for recursive parameterestimation is also investigated. The applications considered are the camera positioning problem arising from augmented reality and sensor fusion problems originating from automotive active safety systems. The use of vision measurements in the estimation problem is central to both applications. In augmented reality, the estimates of the camera’s position and orientation are imperative in the process of overlaying computer generated objects onto the live video stream. The objective in the sensor fusion problems arising in automotive safety systems is to provide information about the host vehicle and its surroundings, such as the position of other vehicles and the road geometry. Information of this kind is crucial for many systems, such as adaptive cruise control, collision avoidance and lane guidance.

L’importance et l’expansion des arboviroses comme la dengue ou le virus Zika nécessite des modèles pour mieux comprendre et prédire leurs dynamiques. La propagation vectorielle de ces maladies est influencée par de multiples facteurs humains et environnementaux qui rendent complexe la construction de modèles épidémiologiques parcimonieux. Parallèlement, de nombreux outils théoriques et computationnels existent désormais pour confronter ces modèles aux données observées. L’objectif de ce travail de thèse est donc d’apporter l’éclairage des données sur les modèles de propagation des arboviroses. Dans un premier temps, il s’agit d’identifier les éléments les plus importants à incorporer pour modéliser les dynamiques de la dengue en milieu rural, dans la région de Kampong Cham (Cambodge). Différents modèles sont comparés, complexifiant à la fois le détail de l’histoire de la maladie et la prise en compte des formes de stochasticité. Dans le cadre déterministe, on a pu souligner l’importance des interactions entre sérotypes, et le faible intérêt pour la représentation explicite des moustiques vecteurs et des individus asymptomatiques. Par ailleurs, la prise en compte des incertitudes indique qu’une large part de la dynamique est capturée seulement par la stochasticité et non par les éléments du squelette déterministe du modèle. Aussi étudie-t-on dans un second temps d’autres aspects de la transmission de la dengue, comme la saisonnalité et la structure spatiale, grâce à des données d’épidémies à Rio de Janeiro (Brésil). Dans un dernier temps, ces méthodes et modèles sont appliqués à l’étude d’un arbovirus émergent, le virus Zika. A partir de données d’épidémies survenues dans le Pacifique, les paramètres-clé de la propagation du virus sont estimés dans le cadre stochastique, et leur variabilité est envisagée à la fois en termes de contexte géographique et de modèle épidémiologique, par la comparaison de quatre îles et de deux modèles à transmission vectorielle. Par ailleurs, la question des interactions potentielles du virus Zika avec celui de la dengue est explorée. / Arboviruses such as the dengue and Zika viruses are expanding worldwide and mo- deling their dynamics can help to better understand and predict their propagation, as well as experiment control scenarios. These mosquito-borne diseases are influenced by a multiplicity of human and environmental factors that are complex to include in parsimonious epidemiological models. In parallel, statistical and computational tools are nowadays available to confront theore- tical models to the observed data. The objective of this PhD work is therefore to study arbovirus propagation models in the light of data. Firstly, in order to identify the most important elements to incorporate in models for dengue dynamics in a rural setting, several dengue models are com- pared using data from the Kampong Cham region in Cambodia. Models incorporate increasing complexity both in the details of disease life history and in the account for several forms of sto- chasticity. In the deterministic framework, including serotype interactions proved decisive, whereas explicit modeling of mosquito vectors and asymptomatic infections had limited added value, when seasonality and underreporting are already accounted for. Moreover, including several forms of un- certainties highlighted that a large part of the disease dynamics is only captured by stochasticity and not by the elements of the deterministic skeleton. Therefore, secondly, we explore other aspects of transmission, such as seasonality and spatial structure, in the case of dengue epidemics in Rio de Janeiro (Brazil). Finally, the models and estimation methods are applied to study an emerging arbovirus, the Zika virus. Using data from epidemics in the Pacific, we estimate the key parameters of disease propagation in the stochastic framework and explore their variability in terms of geogra- phic setting and model formulation by comparing four islands and two models with vector-borne transmission. In addition, potential interactions with the dengue virus are explored.

L'autonomie d'un engin aérospatial requière de disposer d'une boucle de navigation-guidage-pilotage efficace et sûre. Cette boucle intègre des filtres estimateurs et des lois de commande qui doivent dans certains cas s'accommoder de non-linéarités sévères et être capables d'exploiter des mesures ambiguës. De nombreuses approches ont été développées à cet effet et parmi celles-ci, les approches particulaires présentent l'avantage de pouvoir traiter de façon unifiée des problèmes dans lesquels les incertitudes d’évolution du système et d’observation peuvent être soumises à des lois statistiques quelconques. Cependant, ces approches ne sont pas exemptes de défauts dont le plus important est celui du coût de calcul élevé. D'autre part, dans certains cas, ces méthodes ne permettent pas non plus de converger vers une solution acceptable. Des adaptations récentes de ces approches, combinant les avantages du particulaire tel que la possibilité d'extraire la recherche d'une solution d'un domaine local de description et la robustesse des approches ensemblistes, ont été à l'origine du travail présenté dans cette thèse.Cette thèse présente le développement d’un algorithme d’estimation d’état, nommé le Box Regularised Particle Filter (BRPF), ainsi qu’un algorithme de commande, le Box Particle Control (BPC). Ces algorithmes se basent tous deux sur l’utilisation de mixtures de noyaux bornés par des boites (i.e., des vecteurs d’intervalles) pour décrire l’état du système sous la forme d’une densité de probabilité multimodale. Cette modélisation permet un meilleur recouvrement de l'espace d'état et apporte une meilleure cohérence entre la prédite et la vraisemblance. L’hypothèse est faite que les incertitudes incriminées sont bornées. L'exemple d'application choisi est la navigation par corrélation de terrain qui constitue une application exigeante en termes d'estimation d'état.Pour traiter des problèmes d’estimation ambiguë, c’est-à-dire lorsqu’une valeur de mesure peut correspondre à plusieurs valeurs possibles de l’état, le Box Regularised Particle Filter (BRPF) est introduit. Le BRPF est une évolution de l’algorithme de Box Particle Filter (BPF) et est doté d’une étape de ré-échantillonnage garantie et d’une stratégie de lissage par noyau (Kernel Regularisation). Le BRPF assure théoriquement une meilleure estimation que le BPF en termes de Mean Integrated Square Error (MISE). L’algorithme permet une réduction significative du coût de calcul par rapport aux approches précédentes (BPF, PF). Le BRPF est également étudié dans le cadre d’une intégration dans des architectures fédérées et distribuées, ce qui démontre son efficacité dans des cas multi-capteurs et multi-agents.Un autre aspect de la boucle de navigation–guidage-pilotage est le guidage qui nécessite de planifier la future trajectoire du système. Pour tenir compte de l'incertitude sur l'état et des contraintes potentielles de façon versatile, une approche nommé Box Particle Control (BPC) est introduite. Comme pour le BRPF, le BPC se base sur des mixtures de noyaux bornés par des boites et consiste en la propagation de la densité d’état sur une trajectoire jusqu’à un certain horizon de prédiction. Ceci permet d’estimer la probabilité de satisfaire les contraintes d’état au cours de la trajectoire et de déterminer la séquence de futures commandes qui maintient cette probabilité au-delà d’un certain seuil, tout en minimisant un coût. Le BPC permet de réduire significativement la charge de calcul. / State estimation and trajectory planning are two crucial functions for autonomous systems, and in particular for aerospace vehicles.Particle filters and sample-based trajectory planning have been widely considered to tackle non-linearities and non-Gaussian uncertainties.However, these approaches may produce erratic results due to the sampled approximation of the state density.In addition, they have a high computational cost which limits their practical interest.This thesis investigates the use of box kernel mixtures to describe multimodal probability density functions.A box kernel mixture is a weighted sum of basic functions (e.g., uniform kernels) that integrate to unity and whose supports are bounded by boxes, i.e., vectors of intervals.This modelling yields a more extensive description of the state density while requiring a lower computational load.New algorithms are developed, based on a derivation of the Box Particle Filter (BPF) for state estimation, and of a particle based chance constrained optimisation (Particle Control) for trajectory planning under uncertainty.In order to tackle ambiguous state estimation problems, a Box Regularised Particle Filter (BRPF) is introduced.The BRPF consists of an improved BPF with a guaranteed resampling step and a smoothing strategy based on kernel regularisation.The proposed strategy is theoretically proved to outperform the original BPF in terms of Mean Integrated Square Error (MISE), and empirically shown to reduce the Root Mean Square Error (RMSE) of estimation.BRPF reduces the computation load in a significant way and is robust to measurement ambiguity.BRPF is also integrated to federated and distributed architectures to demonstrate its efficiency in multi-sensors and multi-agents systems.In order to tackle constrained trajectory planning under non-Gaussian uncertainty, a Box Particle Control (BPC) is introduced.BPC relies on an interval bounded kernel mixture state density description, and consists of propagating the state density along a state trajectory at a given horizon.It yields a more accurate description of the state uncertainty than previous particle based algorithms.A chance constrained optimisation is performed, which consists of finding the sequence of future control inputs that minimises a cost function while ensuring that the probability of constraint violation (failure probability) remains below a given threshold.For similar performance, BPC yields a significant computation load reduction with respect to previous approaches.

In today's industrial landscape, the pursuit of operational excellence has driven organizations to seek innovative approaches to ensure the uninterrupted functionality of machinery and equipment. Predictive maintenance (PM) provides a pivotal strategy to achieve this goal by detecting faults earlier and predicting maintenance before the system enters a critical state. This thesis proposed a fault detection and diagnosis (FDD) method for predictive maintenance using particle filter resampling and a particle tracking technique. To develop this FDD method, particle filter and hidden Markov model efficiency in the forecasting system state variables are studied on a hydraulic wind power transfer system with different noise levels and system faults. Furthermore, a particle tracker is developed to analyze the particle filter's resampling process and study the particle selection process. After that, the proposed FDD method was developed and validated through three simulation tests employing system degradation models. Furthermore, the system's remaining useful life (RUL) is estimated for those simulation tests.

På senare år har tjuvjakten på noshörningar resulterat i ett kritiskt lågt bestånd. Detta examensarbete är en del av ett initiativ för att stoppa denna utveckling. Målet är att använda en UAV, utrustad med GPS och attitydsensorer, samt en värmekamera placerad på en gimbal, till att övervaka vilda djur. Genom att använda en värmekamera kan djuren lätt detekteras eftersom de antas vara varmare än sin omgivning. En modell av marken vid testområdet har använts för att möjliggöra positionering av detekterade djur, samt analys av vilka områden på marken som ses av kameran. Termen övervakning inkluderar detektion av djur, målföljning och planering av rutt för UAV:n. UAV:n ska kunna söka av ett område efter djur. För att göra detta krävs planering av trajektoria för UAV:n samt hur gimbalen ska förflyttas. Flera metoder för detta har utvärderats. UAV:n ska även kunna målfölja djur som har detekterats. Till detta har ett partikelfilter använts. För att associera mätningar till spår har Nearest Neighbor-metoden använts. Djuren detekteras genom att bildbehandla på videoströmmen som ges från värmekameran. För bildbehandlingen har flertalet metoder testats. Dessutom presenteras en omfattande beskrivning av hur en UAV fungerar och är uppbyggd. I denna beskrivs även nödvändiga delar för ett UAV-system. På grund av begränsningar i budgeten har ingen UAV inköpts. Istället har tester utförts från en gondol i Kolmården. Gondolen åker runt i testområdet med en konstant hastighet. Djur kunde lätt detekteras och målföljas givet en kall bakgrund. Då solen värmer upp marken är det svårare att särskilja djuren från marken och fler feldetektioner görs av bildbehandlingen / In recent years, the poaching of rhinoceros has decreased its numbers to critical levels. This thesis project is a part of an initiative to stop this development. The aim of this master thesis project is to use a UAV equipped with positioning and attitude sensors as well as a thermal camera, placed onto a gimbal, to perform wildlife surveillance. By using a thermal camera, the animals are easily detected as they are assumed to be warmer than the background. The term wildlife surveillance includes detection of animals, tracking, and planning of the UAV. The UAV should be able to search an area for animals, for this planning of the UAV trajectory and gimbal attitude is needed. Several approaches for this have been tested, both online and offline planning. The UAV should also be able to track the animals that are detected, for this a particle filter has been used. Here a problem of associating measurements to tracks arises. This has been solved by using the Nearest Neighbor algorithm together with gating. The animals are detected by performing image processing on the images received from the thermal camera. Multiple approaches have been evaluated. Furthermore, a thoroughly worked description of how a UAV is working as well as how it is built up is presented. Here also necessary parts to make up a full unmanned aerial system are described. This chapter can be seen as a good guide for beginners, to the UAV field, interested in knowing how a UAV works and the most common parts of such a system. A ground model of Kolmården, where the testing has been conducted, has been used in this thesis. The use of this enables positioning of the detected animals and checking if an area is occluded for the camera. Unfortunately, due to budget limitations, no UAV was purchased. Instead, testing has been conducted from a gondola in Kolmården traveling across the test area with a constant speed. To use the gondola as the platform, for the sensors and the thermal camera, is essentially the same as using a UAV as both alternatives are located in the air above the animals, both are traveling around the map and both are stable for good weather conditions. The animals could easily be detected and tracked given a cold background. When the sun heats up the ground, it is harder to distinguish the animals in the thermal video, and more false detections in the image processing appear.

Cyber-Physical Systems theory is a new concept that is about to revolutionize the way computers interact with the physical world by integrating physical knowledge into the computing systems and tailoring such computing systems in a way that is more compatible with the way processes happen in the physical world. In this master’s thesis, Field Programmable Gate Arrays (FPGA) are studied as a potential technological asset that may contribute to the enablement of the Cyber-Physical paradigm. As an example application that may benefit from cyber-physical system support, the Electro-Slag Remelting process - a process for remelting metals into better alloys - has been chosen due to the maturity of its related physical models and controller designs. In particular, the Particle Filter that estimates the state of the process is studied as a candidate for FPGA-based computing enhancements. In comparison with CPUs, through the designs and experiments carried in relationship with this study, the FPGA reveals itself as a serious contender in the arsenal of v computing means for Cyber-Physical Systems, due to its capacity to mimic the ubiquitous parallelism of physical processes. / text