The moon is the brightest celestial body in the night sky, and it is of some value when determining the astronomical azimuth on a cloudy or foggy night or in a light-polluted city when there is no visible star. Based on the edge observations of the apparent moon using a total station, a global positioning system (GPS) receiver, a portable computer, and some appropriate software and instrumentation, an innovative method for determining the center of the apparent moon, is discussed in this paper, and calculation formulas are deduced in detail. Second, errors in the astronomical azimuth determination are quantitatively analyzed. Finally, two practical tests are performed in the central plains of China, and the results indicate that the difference between azimuth determination by Polaris and the moon is up to 7″ from two field tests.

Astronomical positioning and orientation are important components of astronomical geodesy tasks, and they could also be used for real-time celestial navigation. In traditional methods, astronomical positioning and astronomical orientation can only be implemented separately, and all the observations with different levels of precision are given the same weight. To change this, a unified astronomical positioning and orientation model based on robust estimation was developed. First, the problem that the coordinate rotation parameters are difficult to calculate directly was solved by using the Rodrigues matrix. Consequently, the astronomical longitude, latitude, and azimuth can be calculated at the same time. Second, as a result of the introduction of a robust estimation method, specific weight is matched to observations with corresponding precision when calculating the parameters, and thus more accurate astronomical longitude, latitude, and azimuth valuation can be calculated simultaneously. Experiments indicate that the unified model was able not only to achieve astronomical positioning and orientation simultaneously but also improve the accuracy of final results.

Smart tourism is the new frontier field of the tourism research. To solve current problems of smart tourism and tourism geographic information system (GIS), individualized tour guide route plan algorithm based on tourist sight spatial interest field is set up in the study. Feature interest tourist sight extracting matrix is formed and basic modeling data is obtained from mass tourism data. Tourism groups are determined by age index. Different age group tourists have various interests; thus interest field mapping model is set up based on individual needs and interests. Random selecting algorithm for selecting interest tourist sights by smart machine is designed. The algorithm covers all tourist sights and relative data information to ensure each tourist sight could be selected equally. In the study, selected tourist sights are set as important nodes while iteration intervals and sub-iteration intervals are defined. According to the principle of proximity and completely random, motive iteration clusters and sub-clusters are formed by all tourist sight parent nodes. Tourist sight data information and geospatial information are set as quantitative indexes to calculate motive iteration values and motive iteration decision trees of each cluster are formed, and then all motive iteration values are stored in descending order in a vector. For each cluster, there is an optimal motive iteration tree and a local optimal solution. For all clusters, there is a global optimal solution. Simulation experiments are performed and results data as well as motive iteration trees are analyzed and evaluated. The evaluation results indicate that the algorithm is effective for mass tourism data mining. The final optimal tour routes planned by the smart machine are closely related to tourists’ needs, interests, and habits, which are fully integrated with geospatial services. The algorithm is an effective demonstration of the application on mass tourism data mining.

Since the American Mars Exploration Rover Opportunity landed on Mars in 2004, it has travelled more than 40 km, and heading-determination technology based on its sun sensor has played an important role in safe driving of the rover. A high-precision heading-determination method will always play a significant role in the rover’s autonomous navigation system, and the precision of the measured heading strongly affects the navigation results. In order to improve the heading precision to the 1-arcminute level, this paper puts forward a novel calibration algorithm for solving the comparable distortion of large-field sun sensor by introducing an antisymmetric matrix. The sun sensor and inclinometer alignment model are then described in detail to maintain a high-precision horizon datum, and a strict sun image centroid-extraction algorithm combining subpixel edge detection with circle or ellipse fitting is presented. A prototype comprising a sun sensor, electronic inclinometer, and chip-scale atomic clock is developed for testing the algorithms, models, and methods presented in this paper. Three field tests were conducted in different months during 2017. The results show that the precision of the heading determination reaches 0.28–0.97′ (1σ) and the centroid error of the sun image and the sun elevation are major factors that affect the heading precision.