At present, optical autonomous navigation has become a key technologyin deep space exploration programs. Recent studies focus on the problem of orbit de-termination using autonomous navigation, and the choice of filter is one of the mainissues. To prepare for a possible exploration mission to Mars, the primary emphasisof this paper is to evaluate the capability of three filters, the extended Kalman filter（EKF）, unscented Kalman filter （UKF） and weighted least-squares （WLS） algorithm,which have different initial states during the cruise phase. One initial state is assumedto have high accuracy with the support of ground tracking when autonomous navi-gation is operating; for the other state, errors are set to be large without this support.In addition, the method of selecting asteroids that can be used for navigation fromknown lists of asteroids to form a sequence is also presented in this study. The simula-tion results show that WLS and UKF should be the first choice for optical autonomousnavigation during the cruise phase to Mars.
Several mechanical and physical properties of five apple cultivars (Black, Apricot, Jester, Big Ariane and Medium Ariane) had been estimated. The results showed that there were important significant differences among the cultivars in most of the parameters that were measured. Among the cultivars, Black cultivar had the highest fruit mass (207.65 g), followed by Big Ariane (188.34 g) and Medium Ariane (137.49 g). The actual fruit volume (cm3) ranged from 61.77 (Apricot) to 269.67 (Black). The highest geometric, arithmetic, square and equivalent mean diameter values were observed for Big Ariane. The surface area and projected area of cultivars were between 14.53-69 cm2 and 45.56-165.33 cm2, respectively. The maximum coefficient of static friction was obtained on plastic followed by steel, iron and glass; the maximum coefficient of dynamic friction was obtained on glass followed by steel, plastic and iron.
The grain production prediction is one of the most important links in precision agriculture. In the process of grain production prediction, mechanical noise caused by the factors of difference in field topography and mechanical vibration will be mixed in the original signal, which undoubtedly will affect the prediction accuracy. Therefore, in order to reduce the influence of vibration noise on the prediction accuracy, an adaptive Ensemble Empirical Mode Decomposition (EEMD) threshold filtering algorithm was applied to the original signal in this paper: the output signal was decomposed into a finite number of Intrinsic Mode Functions (IMF) from high frequency to low frequency by using the Empirical Mode Decomposition (EMD) algorithm which could effectively restrain the mode mixing phenomenon; then the demarcation point of high and low frequency IMF components were determined by Continuous Mean Square Error criterion (CMSE), the high frequency IMF components were denoised by wavelet threshold algorithm, and finally the signal was reconstructed. The algorithm was an improved algorithm based on the commonly used wavelet threshold. The two algorithms were used to denoise the original production signal respectively, the adaptive EEMD threshold filtering algorithm had significant advantages in three denoising performance indexes of signal denoising ratio, root mean square error and smoothness. The five field verification tests showed that the average error of field experiment was 1.994% and the maximum relative error was less than 3%. According to the test results, the relative error of the predicted yield per hectare was 2.97%, which was relative to the actual yield. The test results showed that the algorithm could effectively resist noise and improve the accuracy of prediction.