A proposed study of multiple scattering through clouds up to 1 THz

A rigorous computation of the electromagnetic field scattered from an atmospheric liquid water cloud is proposed. The recent development of a fast recursive algorithm (Chew algorithm) for computing the fields scattered from numerous scatterers now makes a rigorous computation feasible. A method is presented for adapting this algorithm to a general case where there are an extremely large number of scatterers. It is also proposed to extend a new binary PAM channel coding technique (El-Khamy coding) to multiple levels with non-square pulse shapes. The Chew algorithm can be used to compute the transfer function of a cloud channel. Then the transfer function can be used to design an optimum El-Khamy code. In principle, these concepts can be applied directly to the realistic case of a time-varying cloud (adaptive channel coding and adaptive equalization). A brief review is included of some preliminary work on cloud dispersive effects on digital communication signals and on cloud liquid water spectra and correlations

Blind Deconvolution of Anisoplanatic Images Collected by a Partially Coherent Imaging System

Coherent imaging systems offer unique benefits to system operators in terms of resolving power, range gating, selective illumination and utility for applications where passively illuminated targets have limited emissivity or reflectivity. This research proposes a novel blind deconvolution algorithm that is based on a maximum a posteriori Bayesian estimator constructed upon a physically based statistical model for the intensity of the partially coherent light at the imaging detector. The estimator is initially constructed using a shift-invariant system model, and is later extended to the case of a shift-variant optical system by the addition of a transfer function term that quantifies optical blur for wide fields-of-view and atmospheric conditions. The estimators are evaluated using both synthetically generated imagery, as well as experimentally collected image data from an outdoor optical range. The research is extended to consider the effects of weighted frame averaging for the individual short-exposure frames collected by the imaging system. It was found that binary weighting of ensemble frames significantly increases spatial resolution

Improving Range Estimation of a 3D FLASH LADAR via Blind Deconvolution

The purpose of this research effort is to improve and characterize range estimation in a three-dimensional FLASH LAser Detection And Ranging (3D FLASH LADAR) by investigating spatial dimension blurring effects. The myriad of emerging applications for 3D FLASH LADAR both as primary and supplemental sensor necessitate superior performance including accurate range estimates. Along with range information, this sensor also provides an imaging or laser vision capability. Consequently, accurate range estimates would also greatly aid in image quality of a target or remote scene under interrogation. Unlike previous efforts, this research accounts for pixel coupling by defining the range image mathematical model as a convolution between the system spatial impulse response and the object (target or remote scene) at a particular range slice. Using this model, improved range estimation is possible by object restoration from the data observations. Object estimation is principally performed by deriving a blind deconvolution Generalized Expectation Maximization (GEM) algorithm with the range determined from the estimated object by a normalized correlation method. Theoretical derivations and simulation results are verified with experimental data of a bar target taken from a 3D FLASH LADAR system in a laboratory environment. Additionally, among other factors, range separation estimation variance is a function of two LADAR design parameters (range sampling interval and transmitted pulse-width), which can be optimized using the expected range resolution between two point sources. Using both CRB theory and an unbiased estimator, an investigation is accomplished that finds the optimal pulse-width for several range sampling scenarios using a range resolution metric

Sparsity driven ultrasound imaging

An image formation framework for ultrasound imaging from synthetic transducer arrays based on sparsity-driven regularization functionals using single-frequency Fourier domain data is proposed. The framework involves the use of a physics-based forward model of the ultrasound observation process, the formulation of image formation as the solution of an associated optimization problem, and the solution of that problem through efficient numerical algorithms. The sparsity-driven, model-based approach estimates a complex-valued reflectivity field and preserves physical features in the scene while suppressing spurious artifacts. It also provides robust reconstructions in the case of sparse and reduced observation apertures. The effectiveness of the proposed imaging strategy is demonstrated using experimental data