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首页> 外文期刊>IEEE Transactions on Aerospace and Electronic Systems >Background noise suppression for signal enhancement by novelty filtering
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Background noise suppression for signal enhancement by novelty filtering

机译：通过新颖滤波抑制背景噪声以增强信号

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摘要

The enhancement of weak signals in the presence of background and channel noise is necessary to design a robust automatic signal detection and recognition system. The autoassociative property of neural networks can be used to map the identifying characteristics of input source waveforms or their spectra. This paper is directed at the exploitation of such neural network properties for novelty filtering that improves the detection probability of weak signals by learning and subsequent subtraction of noise background from the input waveform. A neural-network-based preprocessor that learns to selectively filter out the background noise without significantly affecting the signal will be highly useful in solving practical signal enhancement problems. An analytical basis is established for the operation of neural-network-based novelty filters that enhance the signal detectability in the presence of noise background and channel noise.
机译：设计背景稳定的自动信号检测和识别系统，必须在存在背景噪声和信道噪声的情况下增强微弱信号。神经网络的自缔合特性可用于映射输入源波形或其频谱的识别特性。本文针对利用这种神经网络特性进行新颖性过滤，通过学习和随后从输入波形中减去噪声背景来提高微弱信号的检测概率。学会选择性滤除背景噪声而不会明显影响信号的基于神经网络的预处理器在解决实际的信号增强问题中将非常有用。为基于神经网络的新颖滤波器的运行建立了分析基础，该滤波器在存在噪声背景和通道噪声的情况下增强了信号的可检测性。

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