Parallel implementation by the FPGA of phase diversity based on an improved particle swarm optimization algorithm

The phase diversity (PD) algorithm based on population optimization has been widely used in wavefront sensing due to advantages such as a simple optical path, no customized sensors, and low cost. However, this method requires a large amount of computation, and the optimization process is seriously disturbed by local extreme values, with the calculation time increasing with the size of the population. Therefore, it is unsuitable for scenarios with limited computing power and energy consumption, such as space optical systems. The field programmable gate array (FPGA) is a device widely used in the aerospace field with high flexibility, reconfigurability, high reliability, and low power consumption. Based on the characteristics of FPGA parallel computing, this paper analyzes and improves the phase diversity algorithm and the particle swarm optimization (PSO) used for its solution, making it suitable for a parallel algorithm architecture, and finally realizing FPGA board-level verification. The results show that this work can improve the computational speed and performance of the phase diversity algorithm based on population optimization.