Thesis (M. Eng.)(Electronic)--University of Pretoria, 2005. / Summaries in English and Afrikaans. Includes bibliographical references.

Thesis (M.S.)--University of Wisconsin--Madison. / Typescript. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 259-264).

The introduction of digital filtering is a new and exciting approach in analyzing in-situ seismic data. Digital filters are also in the same spirit as the electric cone which replaced the mechanical cone in CPT* testing. That is, it is desirable to automate CPT testing in order to make it less operator dependent and increase the reliability and accuracy. In CPT seismic cone testing seismic waves are generated at the surface and recorded downhole with velocity or acceleration transducers. The seismic receivers record the different seismic wavelets (e.g., SV-waves, P-waves) allowing one to determine shear and compression wave velocities. In order to distinguish the different seismic events, an instrument with fast response time is desired (i.e., high natural frequency and low damping). This type of instrument is characteristic of an accelerometer. The fast response time (small time constant) of an accelerometer results in a very sensitive instrument with corresponding noisy time domain characteristics. One way to separate events is to characterize the signal frequencies and remove unwanted frequencies. Digital filtering is ideal for this application. The techniques of digital filtering introduced in this research are based on frequency domain filtering, where Fast Fourier, Butterworth Filter, and crosscorrelation algorithms are implemented. One based on time domain techniques, where a Kalman Filter is designed to model'the instrument and the physical environment. The crosscorrelation method allows one to focus on a specific wavelet and use all the information of the wavelets present averaging out any noises or irregularities and relying upon dominant responses. The Kalman Filter was applied in a manner in which it modelled the sensors used and the physical environment of the body waves and noise generation. The KF was investigated for its possible application to obtaining accurate estimates on the P-wave and S-wave amplitudes and arrival times. The KF is a very flexible tool which allows one to model the problem considered accurately. In addition, the KF works in the time domain which removes many of the limitations of the frequency domain techniques. The crosscorrelation filter concepts are applied by a program referred to as CROSSCOR. CROSSCOR is a graphics interactive program which displays the frequency spectrums, unfiltered and filtered time series and crosscorrelations on a mainframe graphics terminal which has been adapted to run on the IBM P.C. CROSSCOR was tested for performance by analyzing synthetic and real data. The results from the analysis on both synthetic and real data indicate that CROSSCOR is an accurate and user friendly tool which greatly assists one in obtaining seismic velocities. The performance of the Kalman Filter was analyzed by generating a source wavelet and passing it through the second order instrumentation. The second order response is then fed into the KF with the arrival time and maximum amplitude being determined. The filter was found to perform well and it has much promise in respect that if it is finely turned, it would be possible to obtain arrival times and amplitudes on line resulting in velocities and damping characteristics, respectively. * Cone Penetration Test / Applied Science, Faculty of / Civil Engineering, Department of / Graduate

Estimation methods such as the Kalman filter identify best state estimates based on certain optimality criteria using a model of the system and the observations. A common assumption underlying the estimation is that the noise is Gaussian. In practical systems though, one quite frequently encounters thick-tailed, non-Gaussian noise. Statistically, contamination by this type of noise can be seen as inducing outliers among the data and leads to significant degradation in the KF. While many nonlinear methods to cope with non-Gaussian noise exist, a filter that is robust in the presence of outliers and maintains high statistical efficiency is desired. To solve this problem, a new robust Kalman filter framework is proposed that bounds the influence of observation, innovation, and structural outliers in a discrete linear system. This filter is designed to process the observations and predictions together, making it very effective in suppressing multiple outliers. In addition, it consists of a new prewhitening method that incorporates a robust multivariate estimator of location and covariance. Furthermore, the filter provides state estimates that are robust to outliers while maintaining a high statistical efficiency at the Gaussian distribution by applying a generalized maximum likelihood-type (GM) estimator. Finally, the filter incorporates the correct error covariance matrix that is derived using the GM-estimator's influence function. This dissertation also addresses robust state estimation for systems that follow a broad class of nonlinear models that possess two or more equilibrium points. Tracking state transitions from one equilibrium point to another rapidly and accurately in such models can be a difficult task, and a computationally simple solution is desirable. To that effect, a new robust extended Kalman filter is developed that exploits observational redundancy and the nonlinear weights of the GM-estimator to track the state transitions rapidly and accurately. Through simulations, the performances of the new filters are analyzed in terms of robustness to multiple outliers and estimation capabilities for the following applications: tracking autonomous systems, enhancing actual speech from cellular phones, and tracking climate transitions. Furthermore, the filters are compared with the state-of-the-art, i.e. the <i>H_â</i>-filter for tracking an autonomous vehicle and the extended Kalman filter for sensing climate transitions. / Ph. D.

ITC/USA 2006 Conference Proceedings / The Forty-Second Annual International Telemetering Conference and Technical Exhibition / October 23-26, 2006 / Town and Country Resort & Convention Center, San Diego, California / OFDM is a preferred physical layer for an increasing number of telemetry and LAN applications. However, joint estimation of the multipath channel and frequency offset in OFDM remains a challenging problem. The Unscented Kalman Filter (UKF) is presented to solve the offset/channel tracking problem. The advantages of the UKF are that it is less susceptible to divergence than the EKF, and does not require computation of a Jacobian matrix. A hybrid analysis/simulation approach is developed to rapidly evaluate UKF performance in terms of symbol-error rate and channel/offset error for the 802.11a OFDM format.

Approved for public release; distribution is unlimited / "Human body motion tracking using inertial sensors requires an attitude estimation filter capable of tracking in all orientations. One way to represent orientation is to use Euler angles, but they have singularities and therefore are not suitable for human body tracking applications. Quaternions can be used to represent orientation without incurring singularities. A quaternion-based Kalman filter has been designed for this purpose and implemented in this thesis. Also, a new suboptimal algorithm to compute the quaternions based on magnetometer and accelerometer data is implemented. This new algorithm called "Factored Quaternion Algorithm" is computationally simpler than previous methods and provides a decoupling property from magnetometer and accelerometer data." p. i. / Lieutenant Junior Grade, Mexican Navy

A navigation filter simulation and analysis environment was developed through the integration of DRAGON, a high fidelity real-time PNT sensor measurement source, and Scorpion, a modular navigation filter implementation framework. The envi- ronment allows navigation filters to be prototyped and tested in varying complex scenarios with a configurable set of navigation sensors including GNSS and IMU. An analysis of an EKF using the environment showed the utility and functionality of the system.

A navigation filter simulation and analysis environment was developed through the integration of DRAGON, a high fidelity real-time PNT sensor measurement source, and Scorpion, a modular navigation filter implementation framework. The envi- ronment allows navigation filters to be prototyped and tested in varying complex scenarios with a configurable set of navigation sensors including GNSS and IMU. An analysis of an EKF using the environment showed the utility and functionality of the system.

Thesis. 1975. M.S.--Massachusetts Institute of Technology. Dept. of Ocean Engineering. / Bibliography: leaves 234-235. / by John G. Lundblad. / M.S.

by John Pui-Fai Sum. / Thesis (Ph.D.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (p. 155-[163]). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web.