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.

Target tracking and trajectory prediction are state estimation applications. Popular state estimation techniques include the Kalman Filter (KF), Extended KF (EKF), Unscented KF (UKF), and the Particle Filter (PF). A limitation of these filters is that the model must be largely known; if this is violated, it may cause instability. A filter known as the Smooth Variable Structure Filter (SVSF) has been developed to address modeling errors. It is hypothesized that SVSFs will improve tracking and trajectory prediction performance due to their robustness against modeling uncertainties. To begin, two trajectory prediction algorithms for autonomous driving based on Interacting Multiple Model (IMM) estimation are developed. One combines the IMM and KF, called IMM-KF, and the other combines IMM with the Generalized Variable Boundary Layer - Smooth Variable Structure Filter (GVBL-SVSF), called IMM-GVBL-SVSF. The performance of both algorithms is comparatively analyzed using synthetic and real datasets. A comparison is made to machine learning strategies as well. Moreover, a general framework for SVSF formulation is proposed, putting a subset of SVSF variants under one umbrella. A strategy to combine nonlinear KFs with SVSFs is proposed, which results in six hybrid filters. Since a subset of SVSF variants can be discovered as special cases of these filters, the proposed framework puts these variants under one umbrella. The hybrid filters are applied to perform aircraft target tracking using synthetic radar measurements. Their performance is compared to the EKF, UKF, Cubature KF, PF, and other SVSFs. Furthermore, the covariance is reformulated for the Dynamic Second-Order Smooth Variable Structure Filter. A new PDAF is formulated that uses this covariance. An optimal filter that minimizes the trace of the covariance is also proposed. The new PDAF and the optimal filter are applied to perform aircraft tracking using synthetic radar data, and the performance is compared with other filters. / Thesis / Doctor of Philosophy (PhD) / This thesis proposes novel algorithms for state estimation, target tracking, and trajectory prediction. State estimation refers to estimating variables of a physical system (e.g. car, robot, airplane) that change over-time using sensor measurements. Examples of variables are position, velocity, and acceleration. These variables are state variables and the set of values together form the state. The state is the smallest set of variables that describe the past behavior of a system such that the system's future behavior can be predicted using these variables. The proposed state estimation methods are applied to perform target tracking. Target tracking involves estimating the state variables (e.g. position, velocity, acceleration) of moving objects detected by sensors such as radar, LIDAR, and camera. Trajectory prediction refers to estimating the future values of these variables in the next few seconds. This thesis also proposes trajectory prediction algorithms for autonomous driving, which utilize state estimation.

In this thesis study, single target tracking algorithms including IMM-PDA and IMM-IPDA algorithms / Optimal approaches in multitarget tracking including IMM-JPDA, IMM-IJPDA and IMM-JIPDA algorithms and an example of Linear Multi-target approaches in multitarget tracking including IMM-LMIPDA algorithm have been studied and implemented in MATLAB for comparison. Simulations were carried out in various realistic test scenarios including single target tracking, tracking of multiple targets moving in convoy fashion, two targets merging in a junction, two targets merging-departing in junctions and multitarget tracking under isolated tracks situations. RMSE performance, track loss and computational load evaluations were done for these algorithms under the test scenarios dealing with these situations. Benchmarkings are presented relying on these outcomes.

This thesis presents a comprehensive example framework on how current multiple model state estimation algorithms for jump Markov linear systems can be improved. The possible improvements are categorized as: -Design of multiple model state estimation algorithms using new criteria. -Improvements obtained using existing multiple model state estimation algorithms. In the first category, risk-sensitive estimation is proposed for jump Markov linear systems. Two types of cost functions namely, the instantaneous and cumulative cost functions related with risk-sensitive estimation are examined and for each one, the corresponding multiple model estate estimation algorithm is derived. For the cumulative cost function, the derivation involves the reference probability method where one defines and uses a new probability measure under which the involved processes has independence properties. The performance of the proposed risk-sensitive filters are illustrated and compared with conventional algorithms using simulations. The thesis addresses the second category of improvements by proposing -Two new online transition probability estimation schemes for jump Markov linear systems. -A mixed multiple model state estimation scheme which combines desirable properties of two different multiple model state estimation methods. The two online transition probability estimators proposed use the recursive Kullback-Leibler (RKL) procedure and the maximum likelihood (ML) criteria to derive the corresponding identification schemes. When used in state estimation, these methods result in an average error decrease in the root mean square (RMS) state estimation errors, which is proved using simulation studies. The mixed multiple model estimation procedure which utilizes the analysis of the single Gaussian approximation of Gaussian mixtures in Bayesian filtering, combines IMM (Interacting Multiple Model) filter and GPB2 (2nd Order Generalized Pseudo Bayesian) filter efficiently. The resulting algorithm reaches the performance of GPB2 with less Kalman filters.

An analysis and simulation tool is developed for optimizing system parameters of the monopulse target tracking radar and observing effects of the system parameters on the performance of the system over different scenarios. A monopulse tracking radar is modeled for measuring the performance of the radar with given parameters, during the thesis studies. The radar model simulates the operation of a Class IA type monopulse automatic tracking radar, which uses a planar phased array. The interacting multiple model (IMM) estimator with the Probabilistic Data Association (PDA) technique is used as the tracking filter. In addition to modeling of the tracking radar model, an optimization tool is developed to optimize system parameters of this tracking radar model. The optimization tool implements a Genetic Algorithm (GA) belonging to a GA Toolbox distributed by Department of Automatic Control and System Engineering at University of Sheffield. The thesis presents optimization results over some given optimization scenarios and concludes on effect of tracking filter parameters, beamwidth and dwell interval for the confirmed track.

abstract: Tracking targets in the presence of clutter is inevitable, and presents many challenges. Additionally, rapid, drastic changes in clutter density between different environments or scenarios can make it even more difficult for tracking algorithms to adapt. A novel approach to target tracking in such dynamic clutter environments is proposed using a particle filter (PF) integrated with Interacting Multiple Models (IMMs) to compensate and adapt to the transition between different clutter densities. This model was implemented for the case of a monostatic sensor tracking a single target moving with constant velocity along a two-dimensional trajectory, which crossed between regions of drastically different clutter densities. Multiple combinations of clutter density transitions were considered, using up to three different clutter densities. It was shown that the integrated IMM PF algorithm outperforms traditional approaches such as the PF in terms of tracking results and performance. The minimal additional computational expense of including the IMM more than warrants the benefits of having it supplement and amplify the advantages of the PF. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2015

<p><h1>Abstract</h1></p> <p>As a compact, robust, and reliable power distribution method, hydraulic systems have been used for flight surface control for decades. Electro-hydrostatic Actuator (EHA) is increasingly replacing the conventional valve-controlled system for better performance, lighter weight and higher energy efficiency. The EHA is increasingly being used for flight control. As such its reliability is thereby critical important for flight safety. This research focuses on fault detection and diagnosis (FDD) for the EHA to enable predictive unscheduled maintenance when fault detected at its inception.</p> <p>An EHA prototype previously built at McMaster University is studied in this research and modified to physically simulate two faults conditions pertaining to leakage and friction. Nine different working conditions including normal running and eight fault conditions are simulated. Physical model has been derived mathematically capable of numerically simulating the fault conditions. Furthermore, for comparison, parametric model was obtained through system identification for each fault condition. This comparison revealed that parametric models are not suitable for fault detection and diagnosis due to the computation complexity.</p> <p>The FDD approach in this research uses model-based state estimation using filters. The filter based combined with the Interacting Multiple Model fault detection and diagnosis algorithm is introduced. Based on this algorithm, three FDD strategies are developed using a combination of the Extended Kalman Filter and IMM (IMM-EKF), the Smooth Variable Structure Filter with Varying Boundary and IMM (IMM-SVSF (VBL)), and the Smooth Variable Structure Filter with Fixed Boundary and IMM (IMM-SVSF (FBL)). All the three FDD strategies were implemented on the EHA prototype. Based on the results, the IMM-SVSF (VBL) provided the best performance. It detected and diagnosed faults correctly at high mode probabilities with excellent robustness to modeling uncertainties. It also was able to detect slow growing leakage fault, and predicted the changing trend of fault conditions.</p> / Master of Applied Science (MASc)

Pour un terminal mobile alimenté sur batterie, le rendement de l’amplificateur de puissance (AP) doit êtreoptimisé. Cette optimisation peut rendre non-linéaire la fonction d’amplification de l’AP. Pour compenser lesdistorsions introduites par le caractère non-linéaire de l’AP, un détecteur numérique fondé sur un modèle deVolterra peut être utilisé. Le comportement de l’AP et le canal étant modélisé par le modèle de Volterra, uneapproche par filtrage de Kalman (FK) permet d’estimer conjointement les noyaux de Volterra et les symbolestransmis. Dans ce travail, nous proposons de traiter cette problématique dans le cadre d’une liaison montantedans un contexte radio intelligente (RI). Dans ce cas, des contraintes supplémentaires doivent être prises encompte. En effet, étant donné que la RI peut changer de bande de fréquence de fonctionnement, les nonlinéaritésde l’AP peuvent varier en fonction du temps. Par conséquent, nous proposons de concevoir une postdistorsionnumérique fondée sur une modélisation par modèles multiples combinant plusieurs estimateurs àbase de FK. Les différents FK permettant de prendre en compte les différentes dynamiques du modèle.Ainsi, les variations temporelles des noyaux de Volterra peuvent être suivies tout en gardant des estimationsprécises lorsque ces noyaux sont statiques. Le cas d’un signal monoporteuse est adressé et validé par desrésultats de simulation. Enfin, la pertinence de l’approche proposée est confirmée par des mesures effectuéessur un AP large bande (300-3000) MHz. / For a battery driven terminal, the power amplifier (PA) efficiency must be optimized. Consequently,non-linearities may appear at the PA output in the transmission chain. To compensatethese distortions, one solution consists in using a digital post-distorter based on aVolterra model of both the PA and the channel and a Kalman filter (KF) based algorithm tojointly estimate the Volterra kernels and the transmitted symbols. Here, we suggest addressingthis issue when dealing with uplink cognitive radio (CR) system. In this case, additionalconstraints must be taken into account. Since the CR terminal may switch from one subbandto another, the PA non-linearities may vary over time. Therefore, we propose to designa digital post-distorter based on an interacting multiple model combining various KF basedestimators using different model parameter dynamics. This makes it possible to track thetime variations of the Volterra kernels while keeping accurate estimates when those parametersare static. Furthermore, the single carrier case is addressed and validated by simulationresults. In addition, the relevance of the proposed approach is confirmed by measurementscarried on a (300-3000) MHz broadband PA.

A promising line of research attempts to bridge the gap between detector and tracker by means of considering jointly optimal parameter settings for both of these subsystems. Along this fruitful path, this thesis study focuses on the problem of detection threshold optimization in a tracker-aware manner so that a feedback from the tracker to the detector is established to maximize the overall system performance. Special emphasis is given to the optimization schemes based on two non-simulation performance prediction (NSPP) methodologies for the probabilistic data association filter (PDAF), namely, the modified Riccati equation (MRE) and the hybrid conditional averaging (HYCA) algorithm. The possible improvements are presented in two domains: Non-maneuvering and maneuvering target tracking. In the first domain, a number of algorithmic and experimental evaluation gaps are identified and newly proposed methods are compared with the existing ones in a unified theoretical and experimental framework. Furthermore, for the MRE based dynamic threshold optimization problem, a closed-form solution is proposed. This solution brings a theoretical lower bound on the operating signal-to-noise ratio (SNR) concerning when the tracking system should be switched to the track before detect (TBD) mode. As the improvements of the second domain, some of the ideas used in the first domain are extended to the maneuvering target tracking case. The primary contribution is made by extending the dynamic optimization schemes applicable to the PDAF to the interacting multiple model probabilistic data association filter (IMM-PDAF). Resulting in an online feedback from the filter to the detector, this extension makes the tracking system robust against track losses under low SNR values.