CUDA-Based Method to Boost Target Performance Evaluation of Space Systems for Automatic Mobile Object Identification and Localization

The complex organization and application conditions of space systems for automatic identification and localization of mobile objects, which include the automatic identification system (AIS) and automatic dependent surveillance-broadcast (ADS-B) system, determine the choice of simulation models for the mathematical formalization of their operation. Simulation modeling of satellite constellations capable of receiving, processing, and retransmitting AIS and ADS-B signals can take a significant amount of time when being used to substantiate circuit design solutions for satellites and plans for their further application given a large number of radiation sources to be simulated (e.g., for the AIS, their number exceeds 500 thousand). One of the methods for solving this problem is parallel computing based on the compute unified device architecture (CUDA) technology. However, due to the specificity of machine instruction execution on NVIDIA GPUs, software quality depends heavily on GPU memory allocation efficiency and algorithms for program code execution. In this paper, we propose a method for target performance evaluation of space systems for automatic identification and localization of mobile objects; the method uses massively parallel computations on GPUs to provide a significant reduction in simulation time, which is especially important for multi-satellite constellations. The efficiency of the method is confirmed by model-cybernetic experiments carried out on various software and hardware platforms.

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