Statistical Theory of Optimal Stochastic Signals Processing in Multichannel Aerospace Imaging Radar Systems

The work is devoted to solving current scientific and applied problems of the development of radar imaging methods. These developments are based on statistical theory of optimal signal processing. These developments allow researchers to create coherent high-resolution information-enriched images as well as incoherent images. These methods can be practically applied in multichannel aerospace radars through the proposed programs and algorithms. Firstly, the following models of stochastic signals at the output of multichannel registration regions of scattered electro-magnetic fields, internal noise, and observation equations are developed and their statistical characteristics investigated. For the considered models of observation equations, the likelihood functional is defined. This definition is an important stage in optimizing spatial and temporal signal processing. These signals are distorted by internal receiver noises in radar systems. Secondly, by synthesising and analysing methods of measuring a radar cross section, the problem of incoherent imaging by aerospace radars with planar antenna array is solved. Thirdly, the obtained optimal mathematical operations are physically interpreted. The proposed interpretation helps to implement a quasi-optimal algorithm of radar cross section estimation in aerospace radar systems. Finally, to verify the proposed theory, a semi-natural experiment of real radio holograms processing was performed. These radio holograms are digital recordings of spatial and temporal signals by an airborne synthetic aperture radar (SAR) system. The results of the semi-natural experiment are presented and analysed in the paper. All the calculations, developments and results in this paper can be applied to new developments in areas such as remote sensing or non-destructive testing.