<title>Subpixel resolution for target tracking</title>

Abstract

Optical sensing mechanisms are designed to provide adequate resolution for the images of the intended objects. Often, the image of an object is so small that the resolution of the image falls beyond the resolution of the sensing device, and some method must be used to attain finer resolution. In these cases, a model-based approach, in which a parametric object model is assumed, can attain the desired sub-pixel resolution capabilities. In the model-based approach, the object model is convolved with the optical system and then matched against the limited number of available sensor samples. The unknown parameters of the object model are then determined by an appropriate estimation technique. This study will focus on estimating the two-dimensional location parameters (i.e. (x,y) location ) of a single point source from a limited number of sensor readings. We present a comparative study of three estimation techniques: maximum likelihood, centroiding, and conditional mean. The sub-pixel resolution capability of these techniques will be studied as a function of signal-to-noise ratio (SNR). The Cramer-Rao theoretical lower bound for unbiased estimators is derived for this problem, and it is shown that the maximum likelihood solution attains the Cramer-Rao bound for SNR’s considered. The merits and deficiencies of the three estimation techniques and their applicability to solving the problem for multiple point sources will also be addressed.