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The use of a standalone Global Navigation Satellite System (GNSS) has proved to be insufficient when navigating indoors or in urban canyons due to multipath or obstruction. Recent technological advances in low cost micro-electro-mechanical system (MEMS) – based sensors (like accelerometers, gyroscopes and magnetometers) enabled the development of sensor-based navigation systems. Although MEMS sensors are low-cost, lightweight, small size, and have low-power consumption, they have complex error characteristics. Accurate computation of the heading angle (azimuth) is one of the most important aspects of any navigation system. It can be computed either by gyroscopes or magnetometers. Gyroscopes are inertial sensors that can provide the angular rate from which the heading can be calculated, however, their outputs drift with time. Moreover, the accumulated errors due to mathematical integration, performed to obtain the heading angle, lead to large heading errors. On the other hand, magnetometers do not suffer from drift and the calculation of heading does not suffer from error accumulation. They can provide an absolute heading from the magnetic north by sensing the earth’s magnetic field. However, magnetometer readings are usually affected by magnetic fields, other than the earth magnetic field, and by other error sources; therefore magnetometer calibration is required to use magnetometer as a reliable source of heading in navigation applications. In this thesis, a framework for fast magnetometer calibration is proposed. This framework requires little space coverage with no user involvement in the calibration process, and does not need specific movements to be performed. The proposed techniques are capable of performing both 2-dimensional (2D) and 3-dimensional (3D) calibration for magnetometers. They are developed to consider different scenarios suitable for different applications, and can benefit from natural device movements. Some applications involve tethering the magnetometers to the moving platform (like in cars and machinery applications). Other applications are related to portable navigation (smartphone navigation, whether for pedestrians or while driving). The developed framework was examined through experimental work to verify its performance and robustness. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2013-05-01 00:52:30.274

The subject of this thesis is the development of parametric methods for the calibration of array shape errors. Two physical scenarios are considered, the online calibration (self-calibration) using far-field sources and the offline calibration using near-field sources. The maximum likelihood (ML) estimators are employed to estimate the errors. However, the well-known computational complexity in objective function optimization for the ML estimators demands effective and efficient optimization algorithms. A novel space-alternating generalized expectation-maximization (SAGE)-based algorithm is developed to optimize the objective function of the conditional maximum likelihood (CML) estimator for the far-field online calibration. Through data augmentation, joint direction of arrival (DOA) estimation and array calibration can be carried out by a computationally simple search procedure. Numerical experiments show that the proposed method outperforms the existing method for closely located signal sources and is robust to large shape errors. In addition, the accuracy of the proposed procedure attains the Cram´er-Rao bound (CRB). A global optimization algorithm, particle swarm optimization (PSO) is employed to optimize the objective function of the unconditional maximum likelihood (UML) estimator for the farfield online calibration and the near-field offline calibration. A new technique, decaying diagonal loading (DDL) is proposed to enhance the performance of PSO at high signal-to-noise ratio (SNR) by dynamically lowering it, based on the counter-intuitive observation that the global optimum of the UML objective function is more prominent at lower SNR. Numerical simulations demonstrate that the UML estimator optimized by PSO with DDL is optimally accurate, robust to large shape errors, and free of the initialization problem. In addition, the DDL technique is applicable to a wide range of array processing problems where the UML estimator is employed and can be coupled with different global optimization algorithms.

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

In both RMCS calibration and ACS calibration, the corresponding efficiency and robustness are tested by simulation and real experiments. In the real experiment of ACS calibration, the intrinsic and extrinsic parameters of the ACS are obtained simultaneously by our calibration procedure using the same image sequences, no extra data capturing step is required. The corresponding trajectory is recovered and illustrated using the calibration results of the ACS. Since the estimated translations of different cameras in an MCS may scaled by different scale factors, scale factor estimation algorithms are proposed for non-overlapping view RMCS calibration and ACS calibration respectively. To our knowledge, we are the first to study the calibration of ACS. / In this thesis, we focus on developing robust methods for the MCS calibration problems. In particular, we make two contributions. Firstly, we developed a novel extrinsic calibration method for the non-overlapping view Rigid Multiple Camera System (RMCS) using the kinematic information of the RMCS. The input are only the images captured when the non-overlapping RMCS is moved in an environment with enough static feature points. This assumption is true in many vision tasks such as SFM (Structure from Motion), SLAM (Simultaneous Localization and Map). The output is the extrinsic parameters of the cameras of the RMCS. / Multiple Camera Systems (MCS) have been widely applied in many vision applications and attracted much attention recently. Both intrinsic and extrinsic parameters of an MCS are needed to be calibrated before it is used. / Secondly, we proposed to solve the calibration of a particular model of non-rigid Multiple Camera System, namely, Articulated Camera System (ACS). In an ACS, the cameras are fixed on articulated arms with joints, the relative pose between them may change. Two ACS calibration methods are proposed. In the first approach, we assume the cameras have overlapping views. It uses the feature correspondences between the cameras in the ACS. In the second approach, we assume the cameras have no overlapping view. It requires only the ego-motion information of the cameras and can be used for the calibration of the non-overlapping view ACS. In both methods, the ACS is assumed to have performed general transformations in a static environment. / Chen, Junzhou. / Adviser: Kin Hong Wong. / Source: Dissertation Abstracts International, Volume: 70-06, Section: B, page: 3594. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 102-110). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

In this MS thesis, a redundant flash analog-to-digital converter (ADC) using a ``Split-ADC' calibration structure and lookup-table-based correction is presented. ADC input capacitance is minimized through use of small, power efficient comparators; redundancy is used to tolerate the resulting large offset voltages. Correction of errors and estimation of calibration parameters are performed continuously in the background in the digital domain. The proposed flash ADC has an effective-number-of-bits (ENOB) of 6-bits and is designed for a target sampling rate of 1Gs/s in 180nm CMOS. The calibration algorithm described has been simulated in MATLAB and an FPGA implementation has been investigated.

Digitized by Kansas Correctional Industries

Digitized by Kansas Correctional Industries