NUMERICAL MODELING OF CHARACTERISTICS AND PARAMETERS OF A NOISE RADAR SENSOR FOR EARTH’S SURFACE MAPPING

Subject and Purpose. The work presents numerical modeling results on the characteristics and parameters of a Noise Radar Sensor (NRS) during remote sensing of terrestrial surfaces. The radiometric (passive) mode and the mode with "backlighting" (active) of the mapping scene are considered. Radiometric signals of surfaces at wavelengths of 3.37 and 1.34 mm are used along with echo signals from the same surfaces under their backlighting (or illuminating) with ultra-weak-power quasicontinuous noise-like signals at a wavelength of 1.53 mm. The focus is on developing a numerical modeling technique to calculate the potential input characteristics of the NRS and compare them with the output parameters of the imagery. Methods and Methodology. The obtained output parameters and characteristics of terrestrial surface imagery are analyzed and synthesized for potential NRS embodiments. The airborne NRS carrier is an AN-14 "Bdzhilka" aircraft. Attention is given to atmospheric conditions and limited time of accumulating useful low-contrast radiometric "grass–concrete" signals. Approximate effective specific grass and concrete scattering surfaces are sought under backlighting conditions. Results. The numerical modeling results regarding the characteristics and parameters of the NRS embodiment have been optimized for two operating modes. The range, coverage sector of surfaces, imagery bands, resolution capability, number of Doppler filters at the NRS outputs, and accuracy features have been established in radiometric mode and during the backlighting of mapping surfaces. Conclusions. Numerical modeling has been conducted based on technologically feasible characteristics of the NRS. The key parameters and features of the NRS in radiometric mode and under conditions of mapping scene backlighting have been optimized. We have analyzed the NRS input characteristics in connection with the output parameters of the imagery. The obtained results will allow us to predict the quality of imagery during remote sensing.