In this paper, we introduce a novel method for the high‐accuracy absolute position determination for planetary rovers using the star sensor and inclinometer. We describe the star sensor and inclinometer model and the alignment method for the two sensors. We deduce the compensation algorithm for the atmosphere refraction correction error in detail and provide the rover's position solution, which effectively eliminates the tilt correction error. The experimental site and hardware configuration are introduced, and the experimental steps for the one‐time positioning are described. Three field tests on Earth indicate that the accuracy of the one‐time positioning is higher than 40 m (1σ) using 8 star images and relative inclinometer measurements. Multiple positionings in one night can improve the accuracy to approximately 15 m.

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Object categorization using single-trial electroencephalography (EEG) data measured while participants view images has been studied intensively. In previous studies, multiple event-related potential (ERP) components (e.g., P1, N1, P2, and P3) were used to improve the performance of object categorization of visual stimuli. In this study, we introduce a novel method that uses multiplekernel support vector machine to fuse multiple ERP component features. We investigate whether fusing the potential complementary information of different ERP components (e.g., P1, N1, P2a, and P2b) can improve the performance of four-category visual object classification in single-trial EEGs. We also compare the classification accuracy of different ERP component fusion methods. Our experimental results indicate that the classification accuracy increases through multiple ERP fusion. Additional comparative analyses indicate that the multiple-kernel fusion method can achieve a mean classification accuracy higher than 72 %, which is substantially better than that achieved with any single ERP component feature (55.07 % for the best single ERP component, N1). We compare the classification results with those of other fusion methods and determine that the accuracy of the multiple-kernel fusion method is 5.47, 4.06, and 16.90 % higher than those of feature concatenation, feature extraction, and decision fusion, respectively. Our study shows that our multiplekernel fusion method outperforms other fusion methods and thus provides a means to improve the classification performance of single-trial ERPs in brain-computer interface research.