VAE+CycleD2GAN-based generative method of synchronous solar/planetary spectral pairs for celestial Doppler difference velocimetry navigation

Abstract The measurement of the celestial Doppler difference velocimetry navigation is the Doppler difference between the solar spectrum and the planetary spectrum. The asynchronous solar/planetary spectral pairs in the existing database lead to the difficulty in the verification of the celestial Doppler difference velocimetry navigation. To solve this problem, we propose a VAE + CycleD2GAN-based generative method of synchronous solar/planetary spectral pairs, which fuse variational auto-encoder (VAE), cycle generative adversarial network (CycleGAN), dual discriminator generative adversarial nets (D2GANs). In CycleGAN, asynchronous solar spectra and planetary spectra are used as the training set to generate synchronous solar/planetary spectral pairs. Due to small samples in the existing spectral database, we explore the strong generalization ability of the latent space in VAE to improve CycleGAN and ensure its learning effectiveness. A double discriminator structure is introduced in VAE + CycleGAN to restrain the disadvantage of training instability and mode collapse, which can effectively capture spectral distribution patterns and improve the quality of generated spectra. Experimental results show that, compared with the asynchronous solar/planetary spectral pairs, the VAE + CycleD2GAN-based generative method produces synchronous solar/planetary spectral pairs, improving the velocimetry accuracy of the synchronous ones by more than 50%, and enhancing the position and velocity accuracies of navigation by more than 12% and 33%, respectively. The research demonstrates that the proposed method provides strong technical support for the feasibility of the celestial Doppler difference velocimetry navigation.