As wireless devices become more common, the ability to position a wireless device has become a topic of importance. Accurate positioning through technologies such as the Global Positioning System is possible for outdoor environments. Indoor environments pose a different challenge, and research continues to position users indoors. Due to the prevalence of wireless local area networks (WLANs) in many indoor spaces, it is prudent to determine their capabilities for the purposes of positioning. Signal strength and time based positioning systems have been studied for WLANs. Direction or angle of arrival (AOA) based positioning will be possible with multiple antenna arrays, such as those included with upcoming devices based on the IEEE 802.11n standard. The potential performance of such a system is evaluated. The positioning performance of such a system depends on the accuracy of the AOA estimation as well as the positioning algorithm. Two different maximum-likelihood (ML) derived algorithms are used to determine the AOA of the mobile user: a specialized simple ML algorithm, and the space- alternating generalized expectation-maximization (SAGE) channel parameter estimation algorithm. The algorithms are used to determine the error in estimating AOAs through the use of real wireless signals captured in an indoor office environment. The statistics of the AOA error are used in a positioning simulation to predict the positioning performance. A least squares (LS) technique as well as the popular extended Kalman filter (EKF) are used to combine the AOAs to determine position. The position simulation shows that AOA- based positioning using WLANs indoors has the potential to position a wireless user with an accuracy of about 2 m. This is comparable to other positioning systems previously developed for WLANs.

This research focuses on the development of intelligent fastening tool tracking systems for the automotive industry to identify the fastened bolts. In order to accomplish such a task, the position of the tool tip must be identified because the tool tip position coincides with the head of the fastened bolt while the tool fastens the bolt. The proposed systems utilize an inertial measurement unit (IMU) and another sensor to track the position and orientation of the tool tip. To minimize the position and orientation calculation error, an IMU needs to be calibrated as accurately as possible. This research presents a novel triaxial accelerometer calibration technique that offers a high accuracy. The simulation and experimental results of the accelerometer calibration are presented. To identify the fastening action, an expert system is developed based on the sensor measurements. When a fastening action is identified, the system identifies the fastened bolt by using an expert system based on the position and orientation of the tool tip and the position and orientation of the bolt. Since each fastening procedure needs different accuracies and requirements, three different systems are proposed. The first system utilizes a triaxial magnetometer and an IMU to identify the fastened bolt. This system calculates the position and orientation by using an IMU. An expert system is used to identify the initial position, stationary state, and the fastened bolt. When the tool fastens a bolt, the proposed expert system detects the fastening action by triaxial accelerometer and triaxial magnetometer measurements. When the fastening action is detected, the system corrects the velocity and position error using zero velocity update (ZUPT). By using the corrected tool tip position and orientation, the system can identify the fastened bolts. Then, with the fastened bolt position, the position of the IMU is corrected. When the tool is stationary, the system corrects linear velocity error and reduces the position error. The experimental results demonstrate that the proposed system can identify fastened bolts if the angles of the bolts are different or the bolts are not closely placed. This low cost system does not require a line of sight, but has limited position accuracy. The second system utilizes an intelligent system that incorporates Kalman filters (KFs) and a fuzzy expert system to track the tip of a fastening tool and to identify the fastened bolt. This system employs one IMU and one encoder-based position sensor to determine the orientation and the centre of mass location of the tool. When the KF is used, the orientation error increases over time due to the integration step. Therefore, a fuzzy expert system is developed to correct the tilt angle error and orientation error. When the tool fastens a bolt, the system identifies the fastened bolt by applying the fuzzy expert system. When the fastened bolt is identified, the 3D orientation error of the tool is corrected by using the location and the orientation of the fastened bolt and the position sensor outputs. This orientation correction method results in improved reliability in determining the tool tip location. The fastening tool tracking system was experimentally tested in a lab environment, and the results indicate that such a system can successfully identify the fastened bolts. This system not only has a low computational cost but also provides good position and orientation accuracy. The system can be used for most applications because it provides a high accuracy. The third system presents a novel position/orientation tracking methodology by hybridizing one position sensor and one factory calibrated IMU with the combination of a particle filter (PF) and a KF. In addition, an expert system is used to correct the angular velocity measurement errors. The experimental results indicate that the orientation errors of this method are significantly reduced compared to the orientation errors obtained from an EKF approach. The improved orientation estimation using the proposed method leads to a better position estimation accuracy. The experimental results of this system show that the orientation of the proposed method converges to the correct orientation even when the initial orientation is completely unknown. This new method was applied to the fastening tool tracking system. This system provides good orientation accuracy even when the gyroscopes (gyros hereafter) include a small error. In addition, since the orientation error of this system does not grow over time, the tool tip position drift is limited. This system can be applied to the applications where the bolts are closely placed. The position error comparison results of the second system and the third system are presented in this thesis. The comparison results indicate that the position accuracy of the third system is better than that of the second system because the orientation error does not increase over time. The advantages and limitations of all three systems are compared in this thesis. In addition, possible future work on fastening tool tracking system is described as well as applications that can be expanded by using the KF/PF combination method.

The continuous growth in power system electric grid by adding new substations lead to construct many new transmission lines, transformers, control devices, and circuit breakers to connect the capacity (generators) to the demand (loads). These components will have a very heavy influence on the performance of the electric grid. The renewable technical solutions for these issues can be found by robust algorithms which can give us a full picture of the current state of the electrical network by monitoring the behavior of phase and voltage magnitude. In this thesis, the major idea is to implement several algorithms including weighted least square, extend kalman filter, and interior point method in three different electrical networks including IEEE 14, 30, and 118 to compare the performance of these algorithms which is represented by the behavior of phases and magnitude voltages as well as minimize the residual of the balance load flow real time measurements to distinguish which one is more robust. Also to have a particular understanding of the comparison between unconstraint and constraint algorithms.

Due to their complementary characteristics, Global Positioning System (GPS) is usually integrated with standalone navigation devices like odometers and inertial measurement units (IMU). Recently, intensive research has focused on utilizing Micro-Electro-Mechanical-System (MEMS) grade inertial sensors in the integration because of their low cost. In this study, a reduced inertial sensor system (RISS) is considered. It comprises a MEMS grade single axis gyroscope, the vehicle built-in odometer, and two optional MEMS grade accelerometers. Estimation technique is needed to allow the data fusion of RISS and GPS. With adequate accuracy, Kalman filter (KF) fulfills this requirement if high-end inertial sensors are used. However, due to the inherent error characteristics of MEMS devices, MEMS-based RISS suffers from the non-stationary stochastic sensor errors and nonlinear inertial errors, which cannot be suppressed by KF alone. To solve the problem, Fast Orthogonal Search (FOS), a nonlinear system identification algorithm, is suggested in this research for modeling higher order RISS errors. FOS algorithm has the ability to figure out the system nonlinearity with a tolerance of arbitrary stochastic system noise. Its modeling results can then be used to predict the system dynamics. Motivated by the above merits, a KF/FOS module is proposed. By handling both linear and nonlinear RISS errors, this module targets substantial enhancement of positioning accuracy. To examine the effectiveness of the proposed technique, KF/FOS module is applied on RISS with GPS in a land vehicle for several road test trajectories. Its performance is compared to KF-only method, both assessed with respect to a high-end reference. To evaluate navigation algorithm in real-time vehicle application, a multi-sensor data logger is designed in this research to collect online RISS/GPS data. KF/FOS module is transplanted on an embedded digital signal processor as well. Both the off-line and online results confirm that KF/FOS module outperforms KF-only approach in positioning accuracy. They also demonstrate reliable real-time performance. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2012-01-22 01:26:11.477

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Duration models are often concerned with time intervals between trades, longer durations indicating a lack of trading activities. In this thesis, we study parameter estimation for the Autoregressive Conditional Duration (ACD) and Stochastic Conditional Duration (SCD) models. Maximum likelihood methods can usually be used in the case of ACD models. However, the SCD models are based on the assumption that durations are generated by a dynamic stochastic latent variable which is often perturbed by Exponential, Weibull, Gamma or Log-Normal distributed innovations. This makes the use of maximum likelihood methods difficult. One alternative method of parameter estimation, in this case, consists in using quasi-maximum likelihood after transforming the original nonlinear model into a state-space model and using the Kalman filter, a similar filtering scheme or the Generalized Method of Moments (GMM). We use the nonlinear filter and GMM method to analyze the Quadratic Stochastic Conditional duration model as well.

Cyber Physical System (CPS) is a system where cyber and physical components work in a complex co-ordination to provide better performance. By exploiting the communication infrastructure among the sensors, actuators, and control systems, attackers may compromise the security of a CPS. In this dissertation, security measures for different types of attacks/ faults in two CPSs, water supply system (WSS) and smart grid system, are presented. In this context, I also present my study on energy management in Smart Grid. The techniques for detecting attacks/faults in both WSS and Smart grid system adopt Kalman Filter (KF) and χ2 detector. The χ2 -detector can detect myriad of system fault- s/attacks such as Denial of Service (DoS) attack, short term and long term random attacks. However, the study shows that the χ2 -detector is unable to detect the intelligent False Data Injection attack (FDI). To overcome this limitation, I present a Euclidean detector for smart grid which can effectively detect such injection attacks. Along with detecting attack/faults I also present the isolation of the attacked/faulty nodes for smart grid. For isolation the Gen- eralized Observer Scheme (GOS) implementing Kalman Filter is used. As GOS is effective in isolating attacks/faults on a single sensor, it is unable to isolate simultaneous attacks/faults on multiple sensors. To address this issue, an Iterative Observer Scheme (IOS) is presented which is able to detect attack on multiple sensors. Since network is an integral part of the future CPSs, I also present a scheme for pre- serving privacy in the future Internet architecture, namely MobilityFirst architecture. The proposed scheme, called Anonymity in MobilityFirst (AMF), utilizes the three-tiered ap- proach to effectively exploit the inherent properties of MF Network such as Globally Unique Flat Identifier (GUID) and Global Name Resolution Service (GNRS) to provide anonymity to the users. While employing new proposed schemes in exchanging of keys between different tiers of routers to alleviate trust issues, the proposed scheme uses multiple routers in each tier to avoid collaboration amongst the routers in the three tiers to expose the end users.

Robotar är en självklar del av modern automation och produktion. Användningsområdenaär många och innefattar bland annat repetitiva arbetsuppgifter ochuppgifter som kan vara hälsofarliga för oss människor, så som t.ex. målning,punktsvetsning och materialhantering. Ett problem inom robotik är att noggrant skatta position och orientering för robotensverktyg. Detta examensarbete syftar till att ta fram metoder för dennaskattning baserad på mätningar från en Inertial Measurement Unit (IMU) sommonteras vid robotens verktyg. En IMU är en kombinationsenhet som består av flera sensorer, vanligtvis accelerometeroch gyroskop. Enheten mäter då acceleration och rotationshastighetbaserat på kroppars tröghet. Examensarbetet presenterar tre metoder för att skatta position och orienteringav robotens verktyg. En skattningsmetod endast är baserad på mätningar frånIMU:n, död räkning, samt två filter där även robotkinematiken tillsammans meduppmätta motorvinklar används, extended Kalmanfilter (EKF) och komplementärfilter(CF). Resultat för skattningsmetoderna visas för experimentell data från en högpresterandeIMU tillsammans med en industrirobot med sex frihetsgrader. / Industrial robots have a well established part within modern automation and production.The uses for robots are many and include e.g. repetitive tasks, painting, spot welding and material handling. One problem in robotics is to sufficiently well estimate the position and orientation for the end effector of the robot. This thesis aims to present estimationmethods based on data from an Inertial Measurement Unit (IMU) mounted onthe end effector of the robot. An IMU is a combination unit typically containing accelerometers and gyroscopes.The unit measures acceleration and rotational speed based on the inertia of bodies. The thesis presents three methods for position and orientation estimation. One based exclusively on IMU data, dead reckoning, and two filters based on IMUdata in combination with robot kinematics and motor angles, extended Kalmanfilter (EKF) and complementary filter (CF). Results for the estimation methods are shown based on experimental data froma high-performance IMU and a industrial robot with six degrees of freedom.