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Unsupervised ICA neural networks a

机译:无监督的ICA神经网络

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Abstract: Reticle systems are considered to be the classical approach for estimating the position of a target in a considered field of view an are widely used in IR seekers. Due to the simplicity and low cost, since only a few detectors are used, reticle seekers are still in use and are subject of further research. However, the major disadvantage of reticle trackers has been proven to be sensitivity on the IR countermeasures such as flares and jammers. When redesigned adequately they produce output signals that are linear convolutive combinations of the reticle transmission functions that are considered as the source signals in the context of the Independent Component Analysis (ICA) theory. Each function corresponds with single optical source position. That enables ICA neural network to be applied on the optical tracker output signals giving on its outputs recovered reticle transmission functions. Position of each optical source is obtained by applying appropriate demodulation method on the recovered source signals. The three conditions necessary for the ICA theory to work are shown to be fulfilled in principle for any kind of the reticle geometry. !51
机译:摘要:十字线系统被认为是一种在目标视场中估计目标位置的经典方法,并且在红外搜索器中得到了广泛的应用。由于简单和低成本,由于仅使用了几个检测器,所以标线片搜寻器仍在使用中,并且有待进一步研究。但是,已证明标线跟踪器的主要缺点是对红外对策(例如耀斑和干扰波)敏感。经过适当的重新设计,它们会产生作为标线片传输函数的线性卷积组合的输出信号,在独立分量分析(ICA)理论的背景下,这些信号被视为源信号。每个功能对应一个光源位置。这使得ICA神经网络可以应用于光学跟踪器的输出信号,并在其输出上提供恢复的掩模版传输功能。通过对恢复的源信号应用适当的解调方法,可以获得每个光源的位置。对于任何种类的标线几何,原则上都可以满足ICA理论工作的三个条件。 !51

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