INVESTIGATION OF ALGORITHMS OF DETECTING OF THE CHARACTERISTICS OF REMOTE OBJECTS IN OPTOELECTRONIC SYSTEMS BY THE METHOD OF WAVELET TRANSFORMATION

V. E. Makhov, V. Shirobokov, A. Emelyanov, A. Potapov
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Abstract

The issues of the accuracy of measuring the coordinates and size of objects observed by an optical system by the methods of single and double continuous wavelet transform in their image are considered. It is shown that the use of the second continuous wavelet transform to the curves of the coefficients of the first transform leads to an increase in the extrema of the scalegram and the smoothness of the curves of the coefficients, providing more than two times higher coordinate sensitivity of determining the position and orientation of objects. The use of different types of wavelets in each continuous wavelet transform of signals gives many options for the curves of the coefficients of the continuous wavelet transform and can be used for additional filtering of noise, taking into account the nature of objects. The parallel use of mathematical models and real objects in a neural network for determining the coordinates of signals and their characteristics is proposed, which leads to an increase in accuracy for each type of object, the possibility of constructing intelligent control devices for outer space. Using the example of an experimental installation of two synchronously movable optoelectronic systems, the accuracy of combining images of objects in multiplexing systems from different digital sources is demonstrated.
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基于小波变换的光电系统远程目标特征检测算法研究
研究了用单、双连续小波变换方法测量光学系统观测目标图像的坐标和尺寸的精度问题。结果表明,对第一次连续小波变换的系数曲线进行二次连续小波变换后,尺度图的极值增大,系数曲线的平滑度提高,确定物体位置和方向的坐标灵敏度提高两倍以上。在信号的每个连续小波变换中使用不同类型的小波,为连续小波变换的系数曲线提供了许多选择,并且可以用于额外的噪声滤波,考虑到对象的性质。提出了在神经网络中并行使用数学模型和真实物体来确定信号的坐标及其特征,从而提高了对每种物体的精度,为构建外太空智能控制装置提供了可能。以两个同步移动光电系统的实验装置为例,验证了多路复用系统中不同数字源的物体图像组合的准确性。
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