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An Efficient Nonuniform Channelizer Based on Adjacent Channel Merging

机译:基于相邻信道合并的高效非均匀通道器

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An efficient nonuniform channelization method based on ACM(Adjacent Channel Merging) is proposed to extract large amount of radar signals with different band widths. The neural networks based on cosine basis functions are used to train the LP prototype filter coefficients in order to satisfy the ACM condition. Use this LP prototype filter to form windowed FFT uniform channelizer, then simply add the outputs of the adjacent channels and the nonuniform channelizer can be achieved. The propesed channelizer is simply composed of an FFT component and a few adders, so it has a smaller number of computations than DDC and TPFT channelizers. The computer simulation results given at last shows that the proposed method also has a good channelizing performance.
机译:提出了一种基于ACM(相邻信道合并)的有效的非均匀通道化方法,以提取具有不同带宽的大量雷达信号。基于余弦基函数的神经网络用于训练LP原型滤波器系数以满足ACM条件。使用此LP原型过滤器窗口窗口化FFT均匀通道器,然后只需添加相邻通道的输出,并且可以实现不均匀的通道器。所提出的通道器简单地由FFT组件和一些加法器组成,因此它具有比DDC和TPFT信道的计算较少。最后给出的计算机仿真结果表明,所提出的方法也具有良好的信道性能。

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