Optical System Identification for Passive Electro-Optical Imaging

A statistical inverse-problem approach is presented for jointly estimating camera blur from aliased data of a known calibration target. Specifically, a parametric Maximum Likelihood (ML) PSF estimate is derived for characterizing a camera's optical imperfections through the use of a calibration target in an otherwise loosely controlled environment. The unknown parameters are jointly estimated from data described by a physical forward-imaging model, and this inverse-problem approach allows one to accommodate all of the available sources of information jointly. These sources include knowledge of the forward imaging process, the types and sources of statistical uncertainty, available prior information, and the data itself. The forward model describes a broad class of imaging systems based on a parameterization with a direct mapping between its parameters and physical imaging phenomena. The imaging perspective, ambient light-levels, target-reflectance, detector gain and offset, quantum-efficiency, and read-noise levels are all treated as nuisance parameters. The Cram'{e}r-Rao Bound (CRB) is derived under this joint model, and simulations demonstrate that the proposed estimator achieves near-optimal MSE performance. Finally, the proposed method is applied to experimental data to validate both the fidelity of the forward-models, as well as to establish the utility of the resulting ML estimates for both system identification and subsequent image restoration.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/153395/1/jwleblan_1.pd

Misspecified CRB on Parameter Estimation for a Coupled Mixture of Polynomial Phase and Sinusoidal FM Signals

This paper studies parameter estimation of a coupled mixture of polynomial phase signal (PPS) and sinusoidal frequency modulated (FM) signal, a newly introduced model motivated by industrial applications. Particularly, we analytically evaluate the estimation performance (or performance loss) via the misspecified Cramér-Rao bound (CRB) when system designers choose existing efficient estimation algorithms designed for an independent (decoupled) mixture model due to hardware limits. Our analysis provides an analytical tool to conveniently evaluate performance loss if the implemented system ignores the coupling effect. The achievability of the misspecified CRB is verified by numerical examples