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.

The traditional pulsar period estimation method using a single distorted profile averts multiple epoch folding processes, but still has space for accuracy to improve. To give consideration to both accuracy and computational load, we combine the fast butterfly epoch folding (FBEF) with the single distorted profile (SDP)-based pulsar period estimation, and propose the FBEF-based pulsar period estimation with a few distorted profiles (FBEF-FDP), which merely uses a few epoch folding processes to enhance accuracy sharply. Firstly, the fast butterfly epoch folding is used to produce a few distorted pulsar profiles with different periods. And then the single distorted profile-based pulsar period estimation explores each distorted prolife to provide a corresponding distortion degree. Finally, using these distortion degree values, the nonlinear search method for multiple minimums can get an optimal distortion degree estimation. Besides, we prove that distortion degree estimation errors of different accumulative distorted profiles from the same pulsar photon sequence are incompletely correlated. This is the theorical basis that the distortion degree estimation values from the same pulsar photon sequence can be fitted to obtain more highly accurate distortion degree estimation. The computational complexity demonstrates that the computational load increases with the number of groups. Thus, to ensure the real-time of the FBEF-FDP, the number of groups should not be too large. Simulation results demonstrate that the accuracy of the fast butterfly epoch folding-based pulsar period estimation using a few distorted profiles enhances at the cost of a small computational load.