No description available.

Graduate

This dissertation outlines the development of static and dynamic tomographic wave-front (WF) reconstructors tailored to Multi-Object Adaptive Optics (MOAO). They are applied to Raven, the first MOAO science and technology demonstrator recently installed on an 8m telescope, with the goal of increasing the limiting magnitude in order to increase sky coverage. The results of a new minimum mean-square error (MMSE) solution based on spatio-angular (SA) correlation functions are shown, which adopts a zonal representation of the wave-front and its associated signals. This solution is outlined for the static reconstructor and then extended for the use of standalone temporal prediction. Furthermore, it is implemented as the prediction model in a pupil plane based Linear Quadratic Gaussian (LQG) algorithm. The algorithms have been fully tested in the laboratory and compared to the results from Monte- Carlo simulations of the Raven system. The simulations indicate that an increase in limiting magnitude of up to one magnitude can be expected when prediction is implemented. Two or more magnitudes of improvement may be achievable when the LQG is used. These results are confirmed by laboratory measurements. / Graduate

No description available.

Markov chain Monte Carlo algorithms (MCMC) and Adaptive Markov chain Monte Carlo algorithms (AMCMC) are most important methods of approximately sampling from complicated probability distributions and are widely used in statistics, computer science, chemistry, physics, etc. The core problem to use these algorithms is to build up asymptotic theories for them. In this thesis, we show the Central Limit Theorem (CLT) for the uniformly ergodic Markov chain using the regeneration method. We exploit the weakest uniform drift conditions to ensure the ergodicity and WLLN of AMCMC. Further we answer the open problem 21 in Roberts and Rosenthal [48] through constructing a counter example and finding out some stronger condition which indicates the ergodic property of AMCMC. We find that the conditions (a) and (b) in [46] are not sufficient for WLLN holds when the functional is unbounded. We also prove the WLLN for unbounded functions with some stronger conditions. Finally we consider the practical aspects of adaptive MCMC (AMCMC). We try some toy examples to explain that the general adaptive random walk Metropolis is not efficient for sampling from multi-model targets. Therefore we discuss the mixed regional adaptation (MRAPT) on the compact state space and the modified mixed regional adaptation on the general state space in which the regional proposal distributions are optimal and the switches between different models are very efficient. The theoretical proof is to show that the algorithms proposed here fall within the scope of general theorems that are used to validate AMCMC. As an application of our theoretical results, we analyze the real data about the ``Loss of Heterozygosity" (LOH) using MRAPT.

Adaptive signal processing has evolved in the last thirty years to the point where its use in sensors such as radar and sonar and in communications is indispensable. High frequency (HF) skywave radars benefit in particular from spatial and spatio-temporal adaptive filters, detectors and estimators due to their operation in an environment which is crowded with natural and man-made interferences, as well as significant temporal and spatial distortions due to ionospheric propagation. While adaptive processing is important for other types of sensors, including airborne radars, HF radar systems are particularly well-suited to its application, given the modern digital receiver-per-element arrays and radar facilities able to host large computational resources. This allows use of algorithms viewed as merely theoretical benchmarks for other systems. / However, despite the tremendous advances in radar adaptive signal processing theory since its foundation in the 1960s, a number of important issues have still not been addressed fully. In particular, the problem of limitations in available training data for adaptive estimation has, if anything, become more acute in recent years. In the case of HF radar, the hundreds of degrees of freedom presented by the typical HF array prevent the application of conventional techniques, not because of computational cost, but due to insufficient training sample support. Furthermore, new architectures for next generation systems including two-dimensional transmit and receive antenna arrays with MIMO technology to support non-causal adaptivity on transmit will further increase the demand for training data, making an already significant problem even more important in the future. / The following broad problems are found to be the most important at this stage: Without a prior knowledge of particular radar scenarios, how can the suitability of its adaptively reconstructed model for an associated radar inference be verified; what are the ultimate capabilities of adaptive techniques in the pre-asymptotic domain, beyond which the adaptive detection/ estimation problem cannot provide a consistent solution, and how can that limit be assessed in the absence of defined exact finite-sample statistical properties or by resorting to standard large-sample asymptotics; given a limited training data volume, what is this mix of credible a priori assumptions (parametric models) regarding this radar scenario, on one hand, and its adaptive estimation on the other? / Clearly each of these major questions is too complex to be comprehensively addressed in a single study. But this thesis (and the associated publications), by providing further understandings in each of these areas, introduces important results to the field of adaptive processing in the presence of low training sample support. / Thesis (PhDTelecommunications)--University of South Australia, 2009

Adaptive signal processing has evolved in the last thirty years to the point where its use in sensors such as radar and sonar and in communications is indispensable. High frequency (HF) skywave radars benefit in particular from spatial and spatio-temporal adaptive filters, detectors and estimators due to their operation in an environment which is crowded with natural and man-made interferences, as well as significant temporal and spatial distortions due to ionospheric propagation. While adaptive processing is important for other types of sensors, including airborne radars, HF radar systems are particularly well-suited to its application, given the modern digital receiver-per-element arrays and radar facilities able to host large computational resources. This allows use of algorithms viewed as merely theoretical benchmarks for other systems. / However, despite the tremendous advances in radar adaptive signal processing theory since its foundation in the 1960s, a number of important issues have still not been addressed fully. In particular, the problem of limitations in available training data for adaptive estimation has, if anything, become more acute in recent years. In the case of HF radar, the hundreds of degrees of freedom presented by the typical HF array prevent the application of conventional techniques, not because of computational cost, but due to insufficient training sample support. Furthermore, new architectures for next generation systems including two-dimensional transmit and receive antenna arrays with MIMO technology to support non-causal adaptivity on transmit will further increase the demand for training data, making an already significant problem even more important in the future. / The following broad problems are found to be the most important at this stage: Without a prior knowledge of particular radar scenarios, how can the suitability of its adaptively reconstructed model for an associated radar inference be verified; what are the ultimate capabilities of adaptive techniques in the pre-asymptotic domain, beyond which the adaptive detection/ estimation problem cannot provide a consistent solution, and how can that limit be assessed in the absence of defined exact finite-sample statistical properties or by resorting to standard large-sample asymptotics; given a limited training data volume, what is this mix of credible a priori assumptions (parametric models) regarding this radar scenario, on one hand, and its adaptive estimation on the other? / Clearly each of these major questions is too complex to be comprehensively addressed in a single study. But this thesis (and the associated publications), by providing further understandings in each of these areas, introduces important results to the field of adaptive processing in the presence of low training sample support. / Thesis (PhDTelecommunications)--University of South Australia, 2009

Thesis (Ph. D.)--Ohio University, November, 1990. / Title from PDF t.p.

Thesis (M.S.)--Ohio University, March, 1984. / Title from PDF t.p.

Thesis (M.S.)--Ohio University, March, 1988. / Title from PDF t.p.