Array Processing of Rayleigh Waves for Shear Structure

Site response to earthquakes is strongly dependent on shallow shear wave velocity structure β(z), and evidence suggests that soil strength and liquefaction potential depends on it as well. We have determined β(z) at several sites by inversion of dispersion data from Rayleigh waves recorded on linear arrays of geophones using artificial sources. Improved methods have been developed for extracting phase and group velocities that lead to significantly more stable and accurate inversion results

Sliding window identification with linear-equality constraints

In this paper, we present a new algorithm of sliding window identification with linear-equality constraints. This algorithm consists in firstly deleting the oldest set of data and in secondly adding the last set of data. The method developed in this paper allows to consider at every step a set of new data by an extension of their result. The proposed algorithm is based on the recursive calculus of the pseudo-inverse matrix from the forms of Albert and Sittler. A simple and easily implementable initialization of the constrained algorithm is proposed. An improvement is obtained by removing the influence of oldest set of data and by satisfying the linear-equality constraints. It is shown that the solutions of the sliding window identification algorithm converge to the true parameter that satisfies the equality constraints. Numerical example is provided to show the effectiveness of the proposed method

On improving the performance of the Gauss-Newton filter

Includes abstract.Includes bibliographical references.The Gauss-Newton filter is a tracking filter developed by Norman Morrison around the same time as the celebrated Kalman filter. It received little attention, primarily due to the computation requirements at the time. Today computers have vast processing capacity and computation is no-longer an issue. The filter finite memory length is identified as the key element in the Gauss-Newton filter adaptability and robustness. This thesis focuses on improving the performance of the Gauss-Newton. We incorporate the process noise statistics into the filter algorithm to obtain a filter which explains the error covariance inconsistency of the Kalaman filter. In addition, a biased version of the linear Gauss-Newton filter, with lower mean squared error than the unbiased filter, is proposed. Furthermore the Gauss-Newton filter is adapted using the Levenberg Marquardt method for improved convergence. In order to improve the computation requirements, a recursive version of the filter is obtained

Parameter Identification And Fault Detection For Reliable Control Of Permanent Magnet Motors

The objective of this dissertation is to develop new fault detection, identification, estimation and control algorithms that will be used to detect winding stator fault, identify the motor parameters and optimally control machine during faulty condition. Quality or proposed algorithms for Fault detection, parameter identification and control under faulty condition will validated through analytical study (Cramer-Rao bound) and simulation. Simulation will be performed for three most applied control schemes: Proportional-Integral-Derivative (PID), Direct Torque Control (DTC) and Field Oriented Control (FOC) for Permanent Magnet Machines. New detection schemes forfault detection, isolation and machine parameter identification are presented and analyzed. Different control schemes as PID, DTC, FOC for Control of PM machines have different control loops and therefore it is probable that fault detection and isolation will have different sensitivity. It is very probable that one of these control schemes (PID, DTC or FOC) are more convenient for fault detection and isolation which this dissertation will analyze through computer simulation and, if laboratory condition exist, also running a physical models

Optimization of a sequential alignment verification and positioning system (SAVPS) for proton radiosurgery

Functional proton-beam stereotactic radiosurgery requires sub-millimeter alignment accuracy. A patient tracking system called Sequential Alignment and Position Verification System (SAVPS) is under development at Loma Linda University Medical Center. An optical positioning system (OPS), manufactured by Vicon Peak, has been chosen to verify the correct alignment of the target with the proton beam axis. The main objective of this thesis is to optimize an existing version of SAVPS by conducting error analysis. An image processing algorithm was developed and applied to estimate the error introduced by the Patient Positioning System (PPS) in order to derive the true error of the SAVPS

Best Linear Unbiased Estimation Fusion with Constraints

Estimation fusion, or data fusion for estimation, is the problem of how to best utilize useful information contained in multiple data sets for the purpose of estimating an unknown quantity — a parameter or a process. Estimation fusion with constraints gives rise to challenging theoretical problems given the observations from multiple geometrically dispersed sensors: Under dimensionality constraints, how to preprocess data at each local sensor to achieve the best estimation accuracy at the fusion center? Under communication bandwidth constraints, how to quantize local sensor data to minimize the estimation error at the fusion center? Under constraints on storage, how to optimally update state estimates at the fusion center with out-of-sequence measurements? Under constraints on storage, how to apply the out-of-sequence measurements (OOSM) update algorithm to multi-sensor multi-target tracking in clutter? The present work is devoted to the above topics by applying the best linear unbiased estimation (BLUE) fusion. We propose optimal data compression by reducing sensor data from a higher dimension to a lower dimension with minimal or no performance loss at the fusion center. For single-sensor and some particular multiple-sensor systems, we obtain the explicit optimal compression rule. For a multisensor system with a general dimensionality requirement, we propose the Gauss-Seidel iterative algorithm to search for the optimal compression rule. Another way to accomplish sensor data compression is to find an optimal sensor quantizer. Using BLUE fusion rules, we develop optimal sensor data quantization schemes according to the bit rate constraints in communication between each sensor and the fusion center. For a dynamic system, how to perform the state estimation and sensor quantization update simultaneously is also established, along with a closed form of a recursion for a linear system with additive white Gaussian noise. A globally optimal OOSM update algorithm and a constrained optimal update algorithm are derived to solve one-lag as well as multi-lag OOSM update problems. In order to extend the OOSM update algorithms to multisensor multitarget tracking in clutter, we also study the performance of OOSM update associated with the Probabilistic Data Association (PDA) algorithm

State Estimation with Unconventional and Networked Measurements

This dissertation consists of two main parts. One is about state estimation with two types of unconventional measurements and the other is about two types of network-induced state estimation problems. The two types of unconventional measurements considered are noise-free measurements and set measurements. State estimation with them has numerous real supports. For state estimation with noisy and noise-free measurements, two sequential forms of the batch linear minimum mean-squared error (LMMSE) estimator are obtained to reduce the computational complexity. Inspired by the estimation with quantized measurements developed by Curry [28], under a Gaussian assumption, the minimum mean-squared error (MMSE) state estimator with point measurements and set measurements of any shape is proposed by discretizing continuous set measurements. State estimation under constraints, which are special cases of the more general framework, has some interesting properties. It is found that under certain conditions, although constraints are indispensable in the evolution of the state, update by treating them as measurements is redundant in filtering. The two types of network-induced estimation problems considered are optimal state estimation in the presence of multiple packet dropouts and optimal distributed estimation fusion with transformed data. An alternative form of LMMSE estimation in the presence of multiple packet dropouts, which can overcome the shortcomings of two existing ones, is proposed first. Then under a Gaussian assumption, the MMSE estimation is also obtained based on a hard decision by comparing the measurements at two consecutive time instants. It is pointed out that if this comparison is legitimate, our simple MMSE solution largely nullifies existing work on this problem. By taking linear transformation of the raw measurements received by each sensor, two optimal distributed fusion algorithms are proposed. In terms of optimality, communication and computational requirements, three nice properties make them attractive

Completely Recursive Least Squares and Its Applications

The matrix-inversion-lemma based recursive least squares (RLS) approach is of a recursive form and free of matrix inversion, and has excellent performance regarding computation and memory in solving the classic least-squares (LS) problem. It is important to generalize RLS for generalized LS (GLS) problem. It is also of value to develop an efficient initialization for any RLS algorithm. In Chapter 2, we develop a unified RLS procedure to solve the unconstrained/linear-equality (LE) constrained GLS. We also show that the LE constraint is in essence a set of special error-free observations and further consider the GLS with implicit LE constraint in observations (ILE-constrained GLS). Chapter 3 treats the RLS initialization-related issues, including rank check, a convenient method to compute the involved matrix inverse/pseudoinverse, and resolution of underdetermined systems. Based on auxiliary-observations, the RLS recursion can start from the first real observation and possible LE constraints are also imposed recursively. The rank of the system is checked implicitly. If the rank is deficient, a set of refined non-redundant observations is determined alternatively. In Chapter 4, base on [Li07], we show that the linear minimum mean square error (LMMSE) estimator, as well as the optimal Kalman filter (KF) considering various correlations, can be calculated from solving an equivalent GLS using the unified RLS. In Chapters 5 & 6, an approach of joint state-and-parameter estimation (JSPE) in power system monitored by synchrophasors is adopted, where the original nonlinear parameter problem is reformulated as two loosely-coupled linear subproblems: state tracking and parameter tracking. Chapter 5 deals with the state tracking which determines the voltages in JSPE, where dynamic behavior of voltages under possible abrupt changes is studied. Chapter 6 focuses on the subproblem of parameter tracking in JSPE, where a new prediction model for parameters with moving means is introduced. Adaptive filters are developed for the above two subproblems, respectively, and both filters are based on the optimal KF accounting for various correlations. Simulations indicate that the proposed approach yields accurate parameter estimates and improves the accuracy of the state estimation, compared with existing methods