本文研究了非高斯噪声中信号的检测，采用多层感知器神经网络作为检测器。
In this paper, the authors study the detection of signals in non-Gaussian noise, and employ a multilayer perceptron neural network as a detector.
在非平稳非高斯背景噪声下，使用经典信号检测理论对信号进行检测往往难以达到理想的效果。
If the background noise is nonstationary and non-Gaussian, the effect of classic signal detection theory is not satisfying.
本文将小波包变换用于非高斯噪声统计特性的研究，提出一种新的非高斯分布噪声下的信号检测算法。
In the dissertation, it puts wavelet-packet decomposition into the study on non-gaussian noise. It offers a new signal dection method under non-gaussian noise backgroud.
研究非高斯噪声中微弱瞬态信号的检测。
This paper deals with the detection of weak transient signal buried in non-Gaussian noise.
然后根据离散时间双稳态系统，设计了处理常值二进制信号的接收器结构，在一些非高斯噪声下对接收器的检测性能与匹配滤波器进行了比较分析。
The receiver structure based on discrete-time bistable system is designed for the constant binary signal detection, and it is compared with the matched filter in some cases of non-Gaussian noise.
然后根据离散时间双稳态系统，设计了处理常值二进制信号的接收器结构，在一些非高斯噪声下对接收器的检测性能与匹配滤波器进行了比较分析。
The receiver structure based on discrete-time bistable system is designed for the constant binary signal detection, and it is compared with the matched filter in some cases of non-Gaussian noise.
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