On statistical theory of radar measurements

Abstract

Statistical treatment of radar measurements is important as most radar measurements are corrupted by random receiver noise. In addition to this, many radar targets themselves have to be modeled as random processes. It is therefore not a coincidence that this thesis uses the framework of statistical inverse problems for modeling radar measurements. The introductory part of this thesis first goes through some important mathematical and numerical methods that can be used model radar measurements and to apply these models in practice. We then describe several different types of radar measurements, with emphasis on high power large aperture radars. After this, we go through several useful radar measurement models. Finally, with the help of these models, we discuss optimal experiment design which tupically amounts to radar transmission waveform optimization. The publications included in this thesis contain practical applications of the topics described in the introduction, incluiding amplitude domain estimation of incoherent scatter signals, radar transmission code optimization, inverse synthetic aperture radar, and measurements of space debris.