One of the primary applications of higher order statistics has been for the time delay estimation of non-Gaussian signal in Gaussian spatially correlated noise.
高阶统计量在信号处理中成功的应用例子之一是估计高斯相关噪声中非高斯信号的时延参数。
Fractional lower order moment (FLOM) which also is called as fractional lower order statistics (FLOS) is one forceful tool of non-Gaussian signal analysis and processing.
分数低阶矩(FLOM)或分数低阶统计量(FLOS)是另一种非高斯信号分析处理的有力工具。
By exploiting wavelet packet transform to analyze signals both in time and frequency space, this paper researches the statistic property of non-Gaussian stationary noise and its signal structure.
该文利用小波包变换的时频局部分析能力，研究了非高斯分布平稳随机噪声的统计特性，揭示了非高斯噪声信号的信号结构。
As the ideal mathematical model for non-Gaussian impulsive noise, a -stable distribution has been the focus of intensive research in signal processing fields.
稳定分布作为非高斯脉冲噪声的数学模型，已经成为信号处理领域的热点研究课题。
Another applied in filtering signal containing Gaussian white noise is computed directly by utilizing time-frequency spectrum of GST, which can enhance non-stationary signal and suppress noise.
后者直接应用广义s变换的时频谱实现，用于含高斯白噪声信号的滤波，达到了突出有效信号和压制噪声的效果。
A new hybrid approach to solve the time delay estimation of Gaussian signal in the presence of unknown non-Gaussian spatially correlated noise has been proposed.
介绍了高阶统计量与互相关运算相混合的方法，在对非高斯相关噪声中，高斯信号进行时延估计中的应用。
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.
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。
If the background noise is nonstationary and non-Gaussian, the effect of classic signal detection theory is not satisfying.
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。
This paper deals with the detection of weak transient signal buried in non-Gaussian noise.
研究非高斯噪声中微弱瞬态信号的检测。
Researching non Gaussian signal is a new field of signal processing.
非高斯信号处理是信号处理的一个新领域。
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。
For the case that the measured data contain non-Gaussian latent variables, ICA is more efficient signal extracting algorithm than PCA.
在测量数据含有非高斯潜隐变量的情况下，ICA是比PC A更有效的特征提取算法。
For the case that the measured data contain non-Gaussian latent variables, ICA is more efficient signal extracting algorithm than PCA.
在测量数据含有非高斯潜隐变量的情况下，ICA是比PC A更有效的特征提取算法。
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