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A terminal aimpoint selection algorithm is proposed in this paper for selecting the aimpoint precisely in real time. This approach avoids complicated and time-consuming pattern matching or boundary extraction. Through the image preprocessing and corner detection, the convex hull technique is utilized to cover the target area based on Computation Geometry, where centriod of the convex hull is defined as the aimpoint of the target. The proposed algorithm promotes intelligence, efficiency and accuracy of navigation guidance systems, which is proved in the simulation.

Image segmentation is an important technique for image processing and computer vision. The principles of 1-D Otsu’s algorithm and thresholding through index of fuzziness are described. Since the infrared images of tank have low object-background contrasts and blurred boundaries in the complex background condition, an adaptive algorithm for image thresholding through index of fuzziness, which is combined with the spatial correlative information, is proposed. The new method makes full use of the spatial correlation of pixels, so that it can extract the detail of the image from the complex background effectively, and improve the accuracy of the segmentation. The results of experiments prove that the presented algorithm has better performance and better robustness against noise.

The standard detecting model of the laser radar is given, and the forms of the detecting model for different detecting conditions are analyzed at first. On the base of foregoing analyses, the detecting range models reflecting the main parameters relationship of the laser detector are set up. On the other hand, the detection probability and the false probability are the important indices of overall design for the laser detector, and they are also the important parameters for appraising the reliability of the laser detector. So the mathematical models reflecting the interaction relationship of the detection probability, the false probability, the single-noise ratio and the detecting threshold value are built at the same time, and the Monte Carlo method is adopted to simulate the statistic distributed rules of the detection probabilities, the false probabilities, the single-noise ratio and the detecting threshold values for the laser detector based on the pulse system and the pseudorandom code system. The simulation results show that the ability of the laser detector using pseudorandom code system is better than that using single pulse system. It is proved by design and experiments that the theoretic foundations are provided by the range models and the simulation results for designing scientifically the laser detector.