| 1. | 3 . the detection of delicate signals in chaotic noise is studied
3 、研究了混沌噪声中微弱信号的检测问题。 |
| 2. | Weak signal detection and extraction against the background of chaotic noise based on empirical mode decomposition
基于经验模态分解的混沌噪声背景下弱信号检测与信号提取 |
| 3. | As a result , the extraction of weak signal from strong chaotic noise has been a research focus and also a difficult problem in these recent years
因此,检测湮没在强混沌背景下的微弱信号是目前国内外研究的热点和难点。 |
| 4. | By thorough study of chaos , more and more researches show that a lot of weak signals are embedded in the strong chaotic noise
随着对混沌学的深入研究,越来越多的科学研究表明很多微弱信号湮没在具有混沌行为的背景信号里。 |
| 5. | For the detection problem in chaotic noise ( clutter ) , this paper presents to use the rbf neural network to solve it . analysis of the algorithm is given out
全针对混沌杂波中的信号检测问题，基于神经网络具有的拟和任意非线性函数的能力，建立了混沌系统的神经网络模型。 |
| 6. | Simulation results and real - life data are used to show the feasibility of the proposed method . ls - ar spectrum method and ga - mpsv are presented to solve the estimation problems in chaotic noise
基于混沌噪声的预测值，提出了混沌噪声中的信号检测方案，并进行了相应的仿真试验。 |
| 7. | Do a estimation is often encountered in vary application of radar and communication system . the dissertation then discusses the estimation of interested signals in chaotic noise . we analyzed three different algorithm for this purpose
混沌背景中的有用信号的参数估计问题是在雷达应用(如波达方向估计)和混沌调制的通信系统中窄带干涉抵消中经常遇到的重要问题之一。 |
| 8. | The efficiency of the error - based detecting method when it is used to detect dsss signal in chaotic noise is analyzed theoretically , and simulations are carried out to show its feasibility and efficiency . 4
从理论上分析了用于检测周期信号的基于混沌噪声模型的误差检测方法,对检测混沌噪声中的直扩信号同样有效,并通过仿真实验证实了这种误差检测方法用于检测微弱直扩的可行性和有效性。 |
| 9. | First , calculating time delay and embedding dimension to reconstruct phase space . second , based on chaos theories , the artificial neural network is used to build one - step and multi - steps predictive model . third , combining with an adaptive filter , predictive error is processed so that weak signal is extracted from strong chaotic noise
研究的内容分为三个方面: ( 1 )确定嵌入维数和延迟时间重构相空间; ( 2 )将混沌理论与人工神经网络结合,建立混沌时间序列(混沌背景)的一步与多步预测模型; ( 3 )结合自适应信号分离器对预测误差进行处理达到检测微弱信号的目的。 |
| 10. | This error - based detecting method is improved , that is , predicting the chaotic noise is changed to estimating the chaotic noise . a predicting method is improved to an estimating method to estimate the chaotic noise . then the time used to train the parameters is saved , and no invalid detection will be gotten even if signals come into
4 、改进了误差检测方法,即,将预测混沌噪声改为估计混沌噪声,并改进了一种非线性预测算法,将其用于估计混沌噪声,省去了参数训练的时间,避免了参数尚未训练好时就有信号进入背景噪声中,而使检测无效的情况。 |
