Deep space exploration missions continue to become more ambitious, driving the need to investigate autonomous navigation systems that are accurate and timely. The solar Doppler difference navigation is a newly developed and promising celestial autonomous navigation method for use, particularly, in the crucial capture period. In this paper, we present novel analyses for three error sources for the solar Doppler difference navigation from the perspective of geometry, motivated with a Mars deep space exploration example. The geometry error sources include the area overlap rate of the direct and the reflected solar light sources, the spread effects related to the time difference of arrival (TDOA) of light, and the solar rotation Doppler difference error. The area overlap rate and the spread effects of the TDOA can be utilized to assess the overlap degree of the direct source and the reflected source in both space and time. Theoretical analyses and simulation results demonstrate that the direct and the reflected light sources can be accurately approximated as the same source. The solar rotation Doppler difference error is explored using a velocity error model. This model forms a hemi-ellipsoid that can be utilized to compensate the Doppler error caused by the solar rotation. The three errors decline with the deep space explorer approaching Mars, which means that the performance of the solar Doppler difference navigation method continuously improves in the critical capture period. These results can offer a reference for the system design of the solar Doppler difference navigation.

Due to the complexity and danger of Mars’s environment, traditional Mars unmanned ground vehicles cannot efficiently perform Mars exploration missions. To solve this problem, the DeepLabV3+/Efficientnet hybrid network is proposed and applied to the scene area judgment for the Mars unmanned vehicle system. Firstly, DeepLabV3+ is used to extract the feature information of the Mars image due to its high accuracy. Then, the feature information is used as the input for Efficientnet, and the categories of scene areas are obtained, including safe area, report area, and dangerous area. Finally, according to three categories, the Mars unmanned vehicle system performs three operations: pass, report, and send. Experimental results show the effectiveness of the DeepLabV3+/Efficientnet hybrid network in the scene area judgment. Compared with the Efficientnet network, the accuracy of the DeepLabV3+/Efficientnet hybrid network is improved by approximately 18% and reaches 99.84%, which ensures the safety of the exploration mission for the Mars unmanned vehicle system.

The accuracy of spectral velocimetry is an important performance index for the solar Doppler difference navigation. In order to analyze the influence of spectral variation on the accuracy of velocity measurement, we establish a velocimetry model based on the solar Doppler difference navigation, and propose a velocity accuracy analysis method. In this method, the reflected noise is filtered by wavelet transform, and then the high-resolution cross-correlation is used to solve the redshift value, so as to accurately calculate the velocity of the spacecraft. Combining the existing solar spectrum and Martian reflectance data, the 13 most obvious absorption lines are selected for velocity simulation and calculation. The experimental results show that the velocity accuracy of Doppler difference navigation can reach 0.1312m/s. The width and depth of the spectral line, the planetary reflectivity, as well as the sunlight fluctuations and the reflected noise will all have a certain impact on the spectral velocimetry for the Solar Doppler Difference Navigation.