Modern aircraft are getting more and better sensors. As a result of this, the pilots are getting moreinformation than they can handle. To solve this problem one can automate the information processingand instead provide the pilots with conclusions drawn from the sensor information. An aircraft’smovement can be used to determine which class (e.g. commercial aircraft, large military aircraftor fighter) it belongs to. This thesis focuses on comparing three classification schemes; a Bayesianclassification scheme with uniform priors, Transferable Belief Model and a Bayesian classificationscheme with entropic priors.The target is modeled by a jump Markov linear system that switches between different modes (flystraight, turn left, etc.) over time. A marginalized particle filter that spreads its particles over thepossible mode sequences is used for state estimation. Simulations show that the results from Bayesianclassification scheme with uniform priors and the Bayesian classification scheme with entropic priorsare almost identical. The results also show that the Transferable Belief Model is less decisive thanthe Bayesian classification schemes. This effect is argued to come from the least committed principlewithin the Transferable Belief Model. A fixed-lag smoothing algorithm is introduced to the filter andit is shown that the classification results are improved. The advantage of having a filter that remembersthe full mode sequence (such as the marginalized particle filter) and not just determines the currentmode (such as an interacting multiple model filter) is also discussed.

Tracking of a single ground target using GMTI radar detections is considered. A Variable Structure- Autonomous- Interactive Multiple Model (VS-A-IMM) structure is developed to address challenges of ground target tracking, while maintaining an acceptable level computational complexity at the same time. The following approach is used in this thesis: Use simple tracker structures / incorporate a priori information such as topographic constraints, road maps as much as possible / use enhanced gating techniques to minimize the eect of clutter / develop methods against stop-move motion and hide motion of the target / tackle on-road/o-road transitions and junction crossings / establish measures against non-detections caused by environment. The tracker structure is derived using a composite state estimation set-up that incorporate multi models and MAP and MMSE estimations. The root mean square position and velocity error performances of the VS-A-IMM algorithm are compared with respect to the baseline IMM and the VS-IMM methods found in the literature. It is observed that the newly developed VS-A-IMM algorithm performs better than the baseline methods in realistic conditions such as on-road/o-road transitions, tunnels, stops, junction crossings, non-detections.

Navigation is about knowing your own position, orientation and velocity relative to some geographic entities. The sensor fusion considered in this thesis combines data from a dead reckoning system, inertial navigation system (INS), and measurements of the ground elevation. The very fast dynamics of aircraft navigation makes it difficult to estimate the true states. Instead the algorithm studied will estimate the errors of the INS and compensate for them. A height database is used along with the measurements. The height database is highly non-linear why a Rao-Blackwellized particle filter is used for the sensor fusion. This integrated navigation system only uses data from its own sensors and from the height database, which means that it is independent of information from outside the aircraft. This report will describe the algorithm and illustrate the theory used. The main purpose is to evaluate the algorithm using real flight data, why the result chapter is the most important.

The basic problem with angle-only or bearings-only tracking is to estimate the trajectory of a target (i.e., position and velocity) by using noise corrupted sensor angle data. In this thesis, the tracking platform is an Aerial Vehicle and the target is simulated as another Aerial Vehicle. Therefore, the problem can be defined as a single-sensor bearings only tracking. The state consists of relative position and velocity between the target and the platform. In the case where both the target and the platform travel at constant velocity, the angle measurements do not provide any information about the range between the target and the platform. The platform has to maneuver to be able to estimate the range of the target. Two problems are investigated and tested on simulated data. The first problem is tracking non-maneuvering targets. Extended Kalman Filter (EKF), Range Parameterized Kalman Filter and particle filter are implemented in order to track non-maneuvering targets. As the second problem, tracking maneuvering targets are investigated. An interacting multiple model (IMM) filter and different particle filter solutions are designed for this purpose. Kalman filter covariance matrix initialization and regularization step of the regularized particle filter are discussed in detail.

Interacting Multiple Model (IMM) estimator is used extensively to estimate trajectories of maneuvering targets in cluttered environment. In the standard tracking methods, it is assumed that movement of target is applicable to a certain model and the target could be monitored via the usage of status predictions of that model. However, targets can make different maneuvering movements. At that time, expression of target dynamic model with only one model can be insufficient. In IMM approach, target dynamic model is expressed with more than one model capsulating all maneuvering movements or with one model with different noise level values. This thesis investigates the tracking of the maneuvering ground targets in cluttered environment via IMM estimator with constant velocity model with low/high process noise, coordinated turn model and move-stop-move model. The selection strategies of models are highlighted and the state errors are calculated to evaluate the performance of IMM estimator.

The stringent regulations of emission levels from heavy duty vehicles create a demand for new methods for reducing harmful emissions from diesel engines. This thesis deals with the modelling of the nitrogen oxide (NOx) emissions from heavy duty vehicles using a selective catalyst as an aftertreatment system, utilising ammonia (NH3) for its reduction. The process of the selective catalytic reduction (SCR) is nonlinear, since the result of the chemical reactions involved depends on the load operating point and the temperature. The purpose of this thesis is to investigate different methods for nonlinear system identification of SCR systems with control applications in mind. The main focus of the thesis is on finding suitable techniques for effective NOx reduction without the need of over dosage of ammonia. By using data collected from a simulator together with real measured data, new black-box identification techniques are developed. Scaling and convergence properties of the proposed algorithms are analysed theoretically. Some of the resulting models are used for controller development using e.g. feedback linearisation techniques, followed by validation in a simulator environment. The benefits of nonlinear modelling and control of the SCR system are highlighted in a comparison with control based on linear models of the system. Further, a multiple model approach is investigated for simultaneous control of NOx and tailpipe ammonia. The results indicate an improvement in terms of ammonia slip reduction in comparison with models that do not take the ammonia slip into account. Another approach to NOx reduction is achieved by controlling the SCR temperature using techniques developed for LPV systems. The results indicate a reduction of the accumulated NOx.

La navigation dans les environnements contraints tels que les zones portuaires ou les zones urbainesdenses est souvent exposée au problème du masquage des satellites GPS. Dans ce cas, le recours auxcapteurs proprioceptifs est généralement la solution envisagée pour localiser temporairement le véhiculesur une carte. Cependant, la dérive de ces capteurs met rapidement en défaut le système de navigation.Pour localiser le véhicule, on utilise dans cette thèse, un magnétomètre pour la mesure du cap dans unrepère absolu, un capteur de vitesse et une carte numérique du réseau de routes.Dans ce contexte, le premier apport de ce travail est de proposer la mise en correspondance desmesures de cap avec la carte numérique (map matching) pour localiser le véhicule. La technique proposéefait appel à un filtre particulaire défini dans le domaine circulaire et à un préfiltrage circulairedes mesures de cap. On montre que cette technique est plus performante qu’un algorithme de map matchingtopologique classique et notamment dans le cas problématique d’une jonction de route en Y. Ledeuxième apport de ce travail est de proposer un filtre circulaire multi-modèles CIMM défini dans uncadre bayésien à partir de la distribution circulaire de von Mises. On montre que l’intégration de cettenouvelle approche dans le préfiltrage et l’analyse des mesures de cap permet d’améliorer la robustesse del’estimation de la direction pendant les virages ainsi que d’augmenter la qualité du map matching grâce àune meilleure propagation des particules du filtre sur le réseau de routes. Les performances des méthodesproposées sont évaluées sur des données synthétiques et réelles. / Navigation in constrained areas such as ports or dense urban environments is often exposed to theproblem of non-line-of-sight to GPS satellites. In this case, proprioceptive sensors are generally used totemporarily localize the vehicle on a map. However, the drift of these sensors quickly cause the navigationsystem to fail. To localize the vehicle, a magnetometer is used in this thesis for heading measurementunder an absolute reference together with a velocity sensor and a digital map of the road network.In this context, the first contribution of this work is to provide a matching of the vehicle’s headingwith the digital map (map matching) to localize the vehicle. The proposed technique uses a particle filterdefined in the circular domain and a circular pre-filtering on the heading measurements. It is shown thatthis technique is more efficient than a conventional topological map matching algorithm, particularly inambiguous cases like a Y-shape road junction. The second contribution of this work is to propose a circularmultiple model filter CIMM defined in a Bayesian framwork from the von Mises circular distribution.It is shown that the integration of this new approach in the pre-filtering and analysis of the heading observationsimproves the robustness of the heading’s estimation during cornering and increases the mapmatching’s quality through a better propagation of the particles on the road network. The performancesof the proposed methods are evaluated on synthetic and real data.

This thesis deals with the derivation of novel adaptive feed forward compensator, which will be used for the control of the electromechanical actuators used in automotive industry. The electromechnical actuators are an electronic throttle valve and an EGR valve. The introduced adaptive compensator is derived from an existing multiple model feedback control method. This work describes the derivation of this method and simulation and experimental verification. In addition, the most well known digital filter differentiators are presented and summarized in this paper because the feed forward compensator needs them for its operation. From these filters, one specific is chosen, whoose coefficients for the specific setting leads to integer multiplication and an integer implementation of the filter. This will be used to implement this filter to the FPGA and then we prove, that this implementation saves a lot of FPGA resources compared to filters implemented using fixed or floating-point arithmetic.

Unmanned Underwater Vehicles (UUVs) are widely used in commercial, scientific, and military missions for various purposes. What makes this technology challenging is the increasing mission duration and unknown environment. It is necessary to embed fault-tolerant control paradigms into UUVs to increase the reliability of the vehicles and enable them to execute and finalize complex missions. Specifically, fault-tolerant control (FTC) comprises fault detection, identification, and control reconfiguration for fault compensation. Literature review shows that there have been no systematic methods for fault-tolerant control of UUVs in earlier investigations. This study presents a hierarchical methodology of fault detection, identification and compensation (HFDIC) that integrates these functions systematically in different levels. The method uses adaptive finite-impulse-response (FIR) modeling and analysis in its first level to detect failure occurrences. Specifically, it incorporates a FIR filter for on-line adaptive modeling, and a least-mean-squares (LMS) algorithm to minimize the output error between the monitored system and the filter in the modeling process. By analyzing the resulting adaptive filter coefficients, we extract the information on the fault occurrence. The HFDIC also includes a two-stage design of parallel Kalman filters in levels two and three for fault identification using the multiple-model adaptive estimation (MMAE). The algorithm activates latter levels only when the failure is detected, and can return back to the monitoring loop in case of false failures. On the basis of MMAE, we use multiple sliding-mode controllers and reconfigure the control law with a probability-weighted average of all the elemental control signals, in order to compensate for the fault. We validate the HFDIC on the steering and diving subsystems of Naval Postgraduate School (NPS) UUVs for various simulated actuator and/or sensor failures, and test the hierarchical fault detection and identification (HFDI) with realistic data from at-sea experiment of the Florida Atlantic University (FAU) Autonomous Underwater Vehicles (AUVs). For both occasions, we model actuator and sensor failures as additive parameter changes in the observation matrix and the output equation, respectively. Simulation results demonstrate the ability of the HFDIC to detect failures in real time, identify failures accurately with a low computational overhead, and compensate actuator and sensor failures with control reconfiguration. In particular, verification of HFDI with FAU data confirms the performance of the fault detection and identification methodology, and provides important information on the vehicle performance. / Ph. D.

The problem of aircraft conflict detection and resolution (CDR) in uncertainty is addressed in this thesis. The main goal in CDR is to provide safety for the aircraft while minimizing their fuel consumption and flight delays. In reality, a high degree of uncertainty can exist in certain aircraft-aircraft encounters especially in cases where aircraft do not have the capabilities to communicate with each other. Through the use of a probabilistic approach and a multiple model (MM) trajectory information processing framework, this uncertainty can be effectively handled. For conflict detection, a randomized Monte Carlo (MC) algorithm is used to accurately detect conflicts, and, if a conflict is detected, a conflict resolution algorithm is run that utilizes a sequential list Viterbi algorithm. This thesis presents the MM CDR method and a comprehensive MC simulation and performance evaluation study that demonstrates its capabilities and efficiency.