| 用于通信信号非线性时域滤波的量子随机滤波器 |
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| 引用本文： | 朱仁祥,吴乐南.用于通信信号非线性时域滤波的量子随机滤波器[J].东南大学学报,2007,23(1):22-25. |
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| 作者姓名： | 朱仁祥 吴乐南 |
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| 作者单位： | 东南大学信息科学与工程学院 南京210096 |
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| 基金项目： | 国家自然科学基金，the High Technology Research Program of Jiangsu Province，the Foundation of Excellent Doctoral Dissertation of Southeast University |
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| 摘 要： | 针对通信信号的非线性时域滤波问题,研究了量子随机滤波器的原理和性能.将神经网络与非线性Schroedinger方程相结合,把方程的解作为信号时变的概率密度函数,进而实现滤波功能.研究发现,通过调整势场权系数的取值,可使滤波器具有明显不同的性能.根据此性质,构造了一种新的滤波算法,该算法可使滤波器在信号波形估计的非线性失真程度与它的抗噪能力之间进行折衷,这将大大推广量子随机滤波器的应用,例如,用于通信信号处理.仿真结果表明了量子随机滤波器的优越性能.
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| 关 键 词： | 通信信号处理 非线性滤波 量子随机滤波器 |
| 文章编号： | 24268011 |
| 修稿时间： | 06 13 2006 12:00AM |
Quantum stochastic filters for nonlinear time-domain filtering of communication signals |
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| Zhu Renxiang,Wu Lenan.Quantum stochastic filters for nonlinear time-domain filtering of communication signals[J].Journal of Southeast University(English Edition),2007,23(1):22-25. |
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| Authors: | Zhu Renxiang Wu Lenan |
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| Institution: | School of Information Science and Engineering, Southeast University, Nanjing 210096, China |
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| Abstract: | Principles and performances of quantum stochastic filters are studied for nonlinear time-domain filtering of communication signals.Filtering is realized by combining neural networks with the nonlinear Schroedinger equation and the time-variant probability density function of signals is estimated by solution of the equation.It is shown that obviously different performances can be achieved by the control of weight coefficients of potential fields.Based on this characteristic,a novel filtering algorithm is proposed,and utilizing this algorithm,the nonlinear waveform distortion of output signals and the denoising capability of the filters can be compromised. This will make the application of quantum stochastic filters be greatly extended,such as in applying the filters to the processing of communication signals.The predominant performance of quantum stochastic filters is shown by simulation results. |
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| Keywords: | communication signals processing nonlinear filtering quantum stochastic filters |
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