This thesis evaluates different solutions to the target tracking problem with the use of airborne radar measurements. The purpose of this report is to present and compare options that can improve the tracking performance when the target is performing various manoeuvres while the radar measurements are noisy. A simulation study is done to evaluate and compare the presented solutions, where the evaluating criteria are the estimation errors and the computational complexity. The algorithms investigated are the general pseudo Bayesian of order one (GPB(1)) filter and the interacting multiple model (IMM) filter, each using three motion models, along with several single model Kalman filters. Additionally, the impact on the tracking performance by different choices of radar parameters is also examined. The results show that filters using multiple models are best suited for tracking targets performing different manoeuvres. The tracking performance is improved with both the GPB(1) and IMM algorithms compared to the filters using a single model. Looking at the estimation errors, IMM outperforms the other algorithms and achieves a better general performance for different kinds of manoeuvres. However, IMM have a much higher computational complexity than the filters with a single model. GPB(1) could therefore be more suited for applications where computational power poses a problem, since it is less computationally demanding than IMM. Furthermore, it is shown that different radar parameters have an impact on the tracking performance. The choice of pulse repetition frequency (PRF) and duty cycle used by the radar affects the accuracy of the measurements. The estimation errors of the tracking filters become larger with poor measurements, which also makes it more difficult for the multiple model algorithms to make good use of the different motion models. In most cases, IMM is however less sensitive to the choice of PRF, in relation to how the models are used in the algorithm, compared to GPB(1). Nevertheless, the study shows that there are cases where some combinations of radar parameters drastically reduces the tracking performance and no clear improvement can be seen, not even for IMM.

The lane-keeping system in autonomous vehicles (AV) or even as a part of the advanced driving assistant system (ADAS) is known as one of the primary options of AVs and ADAS. The developed lane-keeping systems work on either computer vision or deep learning algorithms for their lane detection section. However, even the strongest image processing units or the robust deep learning algorithms for lane detection have inaccuracies during lane detection under certain conditions. The source of these inaccuracies could be rainy or foggy weather, high contrast shades of buildings and objects on-street, or faded lines. Since the lane detection unit of these systems is responsible for controlling the steering, even a momentary loss of lane detection accuracy could result in an accident or failure. As mentioned, different lane detection algorithms have been presented based on computer vision and deep learning during the last few years, and each one has pros and cons. Each model may have a better performance in some situations and fail in others. For example, deep learning-based methods are vulnerable to new samples. In this research, multiple models of lane detection are evaluated and used together to implement a robust lane detection algorithm. The purpose of this research is to develop an estimator-based Multiple Model Adaptive Estimation (MMAE) algorithm on the lane-keeping system to improve the robustness of the lane detection system. To verify the performance of the implemented algorithm, the AirSim simulation environment was used. The test simulation vehicle was equipped with one front camera and one back camera used to implement the proposed algorithm. The front camera images are used for detecting the lane and the offset of the vehicle and center point of the lane. The rear camera, which offered better performance in lane detection, was used as an estimator for calculating the uncertainty of each model. The simulation results showed that combining two implemented models with MMAE performed robustly even in those case studies where one of the models failed. The proposed algorithm was able to detect the failures of either of the models and then switch to another good working model to improve the robustness of the lane detection system. However, the proposed algorithm had some limitations; it can be improved by replacing PID controller with an MPC controller in future studies. In addition, in the presented algorithm, two computer vision-based algorithms were used; however, adding a deep learning-based model could improve the performance of the proposed MMAE. To have a robust deep learning-based model, it is suggested to train the network based on AirSim output images. Otherwise, the network will not work accurately due to the differences in the camera's location, camera configuration, colors, and contrast.

Indiana University-Purdue University Indianapolis (IUPUI) / The lane-keeping system in autonomous vehicles (AV) or even as a part of the advanced driving assistant system (ADAS) is known as one of the primary options of AVs and ADAS. The developed lane-keeping systems work on either computer vision or deep learning algorithms for their lane detection section. However, even the strongest image processing units or the robust deep learning algorithms for lane detection have inaccuracies during lane detection under certain conditions. The source of these inaccuracies could be rainy or foggy weather, high contrast shades of buildings and objects on-street, or faded lines. Since the lane detection unit of these systems is responsible for controlling the steering, even a momentary loss of lane detection accuracy could result in an accident or failure. As mentioned, different lane detection algorithms have been presented based on computer vision and deep learning during the last few years, and each one has pros and cons. Each model may have a better performance in some situations and fail in others. For example, deep learning-based methods are vulnerable to new samples. In this research, multiple models of lane detection are evaluated and used together to implement a robust lane detection algorithm. The purpose of this research is to develop an estimator-based Multiple Model Adaptive Estimation (MMAE) algorithm on the lane-keeping system to improve the robustness of the lane detection system. To verify the performance of the implemented algorithm, the AirSim simulation environment was used. The test simulation vehicle was equipped with one front camera and one back camera used to implement the proposed algorithm. The front camera images are used for detecting the lane and the offset of the vehicle and center point of the lane. The rear camera, which offered better performance in lane detection, was used as an estimator for calculating the uncertainty of each model. The simulation results showed that combining two implemented models with MMAE performed robustly even in those case studies where one of the models failed. The proposed algorithm was able to detect the failures of either of the models and then switch to another good working model to improve the robustness of the lane detection system. However, the proposed algorithm had some limitations; it can be improved by replacing PID controller with an MPC controller in future studies. In addition, in the presented algorithm, two computer vision-based algorithms were used; however, adding a deep learning-based model could improve the performance of the proposed MMAE. To have a robust deep learning-based model, it is suggested to train the network based on AirSim output images. Otherwise, the network will not work accurately due to the differences in the camera's location, camera configuration, colors, and contrast.

The current and future trends of modern wireless communication systems place heavy demands on fast data transmissions in order to satisfy end users’ requirements anytime, anywhere. Such demands are obvious in recent applications such as smart phones, long term evolution (LTE), 4 & 5 Generations (4G & 5G), and worldwide interoperability for microwave access (WiMAX) platforms, where robust coding and modulations are essential especially in streaming on-line video material, social media and gaming. This eventually resulted in extreme exhaustion imposed on the frequency spectrum as a rare natural resource due to stagnation in current spectrum management policies. Since its advent in the late 1990s, cognitive radio (CR) has been conceived as an enabling technology aiming at the efficient utilisation of frequency spectrum that can lead to potential direct spectrum access (DSA) management. This is mainly attributed to its internal capabilities inherited from the concept of software defined radio (SDR) to sniff its surroundings, learn and adapt its operational parameters accordingly. CR systems (CRs) may commonly comprise one or all of the following core engines that characterise their architectures; namely, adaptive coded modulation (ACM), automatic modulation classification (AMC) and spectrum sensing (SS). Motivated by the above challenges, this programme of research is primarily aimed at the design and development of new paradigms to help improve the adaptability of CRs and thereby achieve the desirable signal processing tasks at the physical layer of the above core engines. Approximate modelling of Rayleigh and finite state Markov channels (FSMC) with a new concept borrowed from econometric studies have been approached. Then insightful channel estimation by using Kalman filter (KF) augmented with interacting multiple model (IMM) has been examined for the purpose of robust adaptability, which is applied for the first time in wireless communication systems. Such new IMM-KF combination has been facilitated in the feedback channel between wireless transmitter and receiver to adjust the transmitted power, by using a water-filling (WF) technique, and constellation pattern and rate in the ACM algorithm. The AMC has also benefited from such IMM-KF integration to boost the performance against conventional parametric estimation methods such as maximum likelihood estimate (MLE) for channel interrogation and the estimated parameters of both inserted into the ML classification algorithm. Expectation-maximisation (EM) has been applied to examine unknown transmitted modulation sequences and channel parameters in tandem. Finally, the non-parametric multitaper method (MTM) has been thoroughly examined for spectrum estimation (SE) and SS, by relying on Neyman-Pearson (NP) detection principle for hypothesis test, to allow licensed primary users (PUs) to coexist with opportunistic unlicensed secondary users (SUs) in the same frequency bands of interest without harmful effects. The performance of the above newly suggested paradigms have been simulated and assessed under various transmission settings and revealed substantial improvements.

Nombreux sont les problèmes classiquement rencontrés dans les sciences de l'ingénieur dont la résolution fait appel à l'estimation d'état d'un système par le biais d'un observateur. La synthèse d'un observateur n'est envisageable qu'à la condition de disposer d'un modèle à la fois exploitable et représentatif du comportement dynamique du système. Or, la modélisation du système et la synthèse de l'observateur deviennent des tâches difficiles à accomplir dès lors que le comportement dynamique du système doit être représenté par un modèle de nature non linéaire. Face à ces difficultés, l'approche multimodèle peut être mise à profit. Les travaux présentés dans cette thèse portent sur les problèmes soulevés par l'identification, l'estimation d'état et le diagnostic de systèmes non linéaires représentés à l'aide d'un multimodèle découplé. Ce dernier, composé de sous-modèles qui peuvent être de dimensions différentes, est doté d'un haut degré de généralité et de flexibilité et s'adapte particulièrement bien à la modélisation des systèmes complexes à structure variable. Cette caractéristique le démarque des approches multimodèles plus conventionnelles qui ont recours à des sous-modèles de même dimension. Après une brève introduction à l'approche multimodèle, le problème de l'estimation paramétrique du multimodèle découplé est abordé. Puis sont présentés des algorithmes de synthèse d'observateurs d'état robustes vis-à-vis des perturbations, des incertitudes paramétriques et des entrées inconnues affectant le système. Ces algorithmes sont élaborés à partir de trois types d'observateurs dits à gain proportionnel, à gain proportionnel-intégral et à gain multi-intégral. Enfin, les différentes phases d'identification, de synthèse d'observateurs et de génération d'indicateurs de défauts sont illustrées au moyen d'un exemple académique de diagnostic du fonctionnement d'un bioréacteur / The state estimation of a system, with the help of an observer, is largely used in many practical situations in order to cope with many classic problems arising in control engineering. The observer design needs an exploitable model able to give an accurate description of the dynamic behaviour of the system. However, system modelling and observer design can not easily be accomplished when the dynamic behaviour of the system must be described by non linear models. The multiple model approach can be used to tackle these difficulties. This thesis deals with black box modelling, state estimation and fault diagnosis of nonlinear systems represented by a decoupled multiple model. This kind of multiple model provides a high degree of generality and flexibility in the modelling stage. Indeed, the decoupled multiple model is composed of submodels which dimensions can be different. Thus, this feature is a significant difference between the decoupled multiple model and the classical used multiple model where all the submodels have the same dimension. After a brief introduction to the multiple model approach, the parametric identification problem of a decoupled multiple model is explored. Algorithms for robust observers synthesis with respect to perturbations, modelling uncertainties and unknown inputs are afterwards presented. These algorithms are based on three kinds of observers called proportional, proportional-integral and multiple-integral. Lastly, identification, observers synthesis and fault sensitivity signals generation are illustrated via a simulation example of a bioreactor

Táto práca sa zaoberá odstraňovaním šumu, ktorý vzniká z tzv. multilateračných meraní leteckých cieľov. Na tento účel bude využitá najmä teória Bayesovských odhadov. Odvodí sa aposteriórna hustota skutočnej (presnej) polohy lietadla. Spolu s polohou (alebo aj rýchlosťou) lietadla bude odhadovaná tiež geometria trajektórie lietadla, ktorú lietadlo v aktuálnom čase sleduje a tzv. procesný šum, ktorý charakterizuje ako moc sa skutočná trajektória môže od tejto líšiť. Odhad spomínaného procesného šumu je najdôležitejšou časťou tejto práce. Je odvodený prístup maximálnej vierohodnosti a Bayesovský prístup a ďalšie rôzne vylepšenia a úpravy týchto prístupov. Tie zlepšujú odhad pri napr. zmene manévru cieľa alebo riešia problém počiatočnej nepresnosti odhadu maximálnej vierohodnosti. Na záver je ukázaná možnosť kombinácie prístupov, t.j. odhad spolu aj geometrie aj procesného šumu.

With the expected growth of Unmanned Aerial Vehicle (UAV) traffic in the coming years, the demand for UAV tracking solutions in the Air Traffic Control (ATC) industry has been incentivized. To ensure the safe integration of UAVs into airspace, Air Traffic Management (ATM) systems will need to provide a number of services such as UAV tracking. The Interacting Multiple Model Extended Kalman Filter (IMM-EKF) is an industry standard for aircraft tracking, but no such algorithm has been tried and tested for UAV tracking. This thesis aims to determine a suitable tracking algorithm for the specific case of non-cooperative tracking of UAVs from radar data. In non-cooperative tracking scenarios, we do not have any information regarding the UAV other than radar measurements indicating the target’s position. We investigate an Adaptive Interacting Multiple Model Unscented Kalman Filter (IMM-UKF) method with three different motion model combinations in addition to comparing a Cartesian vs. Spherical measurement model. A comparison of motion models shows that using a Constant Jerk (CJ) model to model target maneuvers in the IMM structure reduces the risk of filter divergence as compared to using a turn model, such as Constant Turn (CT) or Constant Angular Velocity (CAV). The CJ model is thus a suitable choice to have as one of the motion models in an IMM structure and works well in conjunction with two Constant Velocity (CV) models. We were not able to determine if the Spherical measurement model is better than the Cartesian measurement model in general. However, the Spherical measurement model improves the accuracy of the state estimate in some cases. Adaptive tuning of the system noise covariance Q and measurement noise covariance R does not improve the accuracy of the state estimate but it improves the filter robustness and consistency when the filter is incorrectly tuned. Based on our results, we believe that the adaptive IMM-UKF shows promise but that there is still room for improvement with regards to both the accuracy and consistency. However, we will need to perform extensive tests with real UAV radar data to draw concrete conclusions. / Med den förväntade tillväxten av trafik med obemannade flygfordon (UAV) under de kommande åren kommer efterfrågan för spårningslösningar för UAV inom flygövervakning. För att säkerställa en säker integration av UAV:er i luftrummet, kommer Air Traffic Management (ATM)-system att behöva tillhandahålla tjänster för UAV-spårning. Det så kallade Interacting Multiple Model Extended Kalman Filter (IMM-EKF) filtret är en industristandard spårning av flygplan, men ingen sådan algoritm har prövats och testats för UAV-spårning. Denna avhandling syftar till att fastställa en lämplig spårningsalgoritm för det specifika fallet med icke samarbetande spårning av UAV från radardata. I icke samarbetande spårningsscenarier har vi ingen information om UAV:n utöver radarmätningar. Vi presenterar en adaptiv metod baserad på IMM-UKF, där vi ersätter EKF i industristandarden IMM-EKF med ett filter av typen UKF. Vi undersöker tre olika kombinationer av rörelsemodeller och jämför också en kartesisk med en sfärisk mätmodell. Vår jämförelse av rörelsemodeller visar om man använder en Constant Jerk (CJ) modell för manövrar i IMM-strukturen minskar risken för divergens jämfört med att använda en svängmodell, såsom Constant Turn (CT) eller Constant Angular Velocity (CAV). CJ-modellen är alltså ett lämpligt val att ha som en av rörelsemodellerna i en IMM-struktur och fungerar bra i kombination med två Constant Velocity (CV) modeller. Vi kunde inte avgöra om den sfäriska modellen var bättre än den kartesiska modellen. Adaptiv inställning av systembrusets kovarians Q och mätbrus kovarians R förbättrar inte tillståndsuppskattningens noggrannhet men den förbättrar filtrets robusthet och konsistens när filtret är felaktigt inställt. Baserat på våra resultat tror vi att den adaptiva IMM-UKF metoden är lovande men att det fortfarande finns utrymme för förbättringar när det gäller både noggrannhet och konsistens i spårningen. Vi kommer dock att behöva utföra omfattande tester med riktiga UAV-radardata för att dra konkreta slutsatser.

This thesis investigates using adaptive estimation techniques to determine unknown model parameters such as size and surface material reflectivity, while estimating position, velocity, attitude, and attitude rates of a resident space object. This work focuses on the application of these methods to the space situational awareness problem. This thesis proposes a unique method of implementing a top-level gating network in a dual-layer hierarchical mixture of experts. In addition it proposes a decaying learning parameter for use in both the single layer mixture of experts and the dual-layer hierarchical mixture of experts. Both a single layer mixture of experts and dual-layer hierarchical mixture of experts are compared to the multiple model adaptive estimation in estimating resident space object parameters such as size and reflectivity. The hierarchical mixture of experts consists of macromodes. Each macromode can estimate a different parameter in parallel. Each macromode is a single layer mixture of experts with unscented Kalman filters used as the experts. A gating network in each macromode determines a gating weight which is used as a hypothesis tester. Then the output of the macromode gating weights go to a top level gating weight to determine which macromode contains the most probable model. The measurements consist of astrometric and photometric data from non-resolved observations of the target gathered via a telescope with a charge coupled device camera. Each filter receives the same measurement sequence. The apparent magnitude measurement model consists of the Ashikhmin Shirley bidirectional reflectance distribution function. The measurements, process models, and the additional shape, mass, and inertia characteristics allow the algorithm to predict the state and select the most probable fit to the size and reflectance characteristics based on the statistics of the measurement residuals and innovation covariance. A simulation code is developed to test these adaptive estimation techniques. The feasibility of these methods will be demonstrated in this thesis.

In many practical problems, both decision and estimation are involved. This dissertation intends to study the relationship between decision and estimation in these problems, so that more accurate inference methods can be developed. Hybrid estimation is an important formulation that deals with state estimation and model structure identification simultaneously. Multiple-model (MM) methods are the most widelyused tool for hybrid estimation. A novel approach to predict the Internet end-to-end delay using MM methods is proposed. Based on preliminary analysis of the collected end-to-end delay data, we propose an off-line model set design procedure using vector quantization (VQ) and short-term time series analysis so that MM methods can be applied to predict on-line measurement data. Experimental results show that the proposed MM predictor outperforms two widely used adaptive filters in terms of prediction accuracy and robustness. Although hybrid estimation can identify model structure, it mainly focuses on the estimation part. When decision and estimation are of (nearly) equal importance, a joint solution is preferred. By noticing the resemblance, a new Bayes risk is generalized from those of decision and estimation, respectively. Based on this generalized Bayes risk, a novel, integrated solution to decision and estimation is introduced. Our study tries to give a more systematic view on the joint decision and estimation (JDE) problem, which we believe the work in various fields, such as target tracking, communications, time series modeling, will benefit greatly from. We apply this integrated Bayes solution to joint target tracking and classification, a very important topic in target inference, with simplified measurement models. The results of this new approach are compared with two conventional strategies. At last, a surveillance testbed is being built for such purposes as algorithm development and performance evaluation. We try to use the testbed to bridge the gap between theory and practice. In the dissertation, an overview as well as the architecture of the testbed is given and one case study is presented. The testbed is capable to serve the tasks with decision and/or estimation aspects, and is helpful for the development of the JDE algorithms.

<p>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. </p><p>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.</p>