A moving target should be missing from a photoelectric theodolite tracker, when the clouds and other special conditions encountered in the course of a theodolite tracking a moving object, and this condition should cause the interruption of tracking process. In view of this problem, an algorithm based on the frame of parameter identification and rolling prediction to trajectory was presented to predicting the target trajectory when it missing. Firstly, the article makes a specification of photoelectric theodolite and it operating mechanism detailed. The reasons of flying target imaging disappear from the field of theodolite telescope and the traditional solution to this problem, the least square curve fitting of trajectory quadratic function of time, were narrated secondly. The algorithm based on recursive least square with forget factor, identify the parameters of target motion using the data of position from single theodolite, then the forecasting trajectory of moving targets was presented afterwards ,in the filtering approach of past data rolling smooth with the weight of last procedure. By simulation with tracking moving targets synthetic corner from a real tracking routine of photoelectric theodolite, the algorithm was testified, and the simulation of curve fitting a quadratic function of time was compared at the last part.
An image sharpness assessment method based on the property of Contrast Sensitivity Function (CSF) was proposed to realize the sharpness assessment of unfocused image. Firstly, image was performed the two-dimensional Discrete Fourier Transform (DFT), and intermediate frequency coefficients and high frequency coefficients are divided into two parts respectively. Secondly the four parts were performed the inverse Discrete Fourier Transform (IDFT) to obtain subimages. Thirdly, using Range Function evaluates the four sub-image sharpness value. Finally, the image sharpness is obtained through the weighted sum of the sub-image sharpness value. In order to comply with the CSF characteristics, weighting factor is setting based on the Contrast Sensitivity Function. The new algorithm and four typical evaluation algorithm: Fourier, Range , Variance and Wavelet are evaluated based on the six quantitative evaluation index, which include the width of steep part of focusing curve, the ration of sharpness, the steepness, the variance of float part of focusing curve, the factor of local extreme and the sensitivity. On the other hand, the effect of noise, and image content on algorithm is analyzed in this paper. The experiment results show that the new algorithm has better performance of sensitivity, anti-nose than the four typical evaluation algorithms. The evaluation results are consistent with human visual characteristics.