[1]王友国[][],潘慧[],刘健[]. 加性乘性噪声改善多元信号检测[J].计算机技术与发展,2016,26(10):160-164.
 WANG You-guo[][],PAN Hui[],LIU Jian[]. Improvement of Multiple Signal Detection by Additive and Multiplicative Noise[J].,2016,26(10):160-164.
点击复制

 加性乘性噪声改善多元信号检测()
分享到:

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
26
期数:
2016年10期
页码:
160-164
栏目:
应用开发研究
出版日期:
2016-10-10

文章信息/Info

Title:
 Improvement of Multiple Signal Detection by Additive and Multiplicative Noise
文章编号:
1673-629X(2016)10-0160-05
作者:
 王友国[1][2] 潘慧[2] 刘健[2]
1. 江苏省物联网技术与应用协同创新中心;2. 南京邮电大学 通信与信息工程学院
Author(s):
 WANG You-guo[1][2] PAN Hui[2] LIU Jian[2]
关键词:
 随机共振多元信号检测错误检测概率乘性噪声
Keywords:
 stochastic resonancemultiple signal detectionprobability of detection errormultiplicative noise
分类号:
TP39
文献标志码:
A
摘要:
 基于最大后验概率准则,以错误检测概率为测度,研究了加性噪声和乘性噪声共同作用下信号检测的问题。在乘性噪声强度不变的情况下,当信号是阈上时,错误检测概率随着加性高斯噪声强度的增加而单调增加,噪声总是干扰信号检测;当信号是阈下时,错误检测概率随着加性高斯噪声强度的增加而逐渐降低至一个最小值后再缓慢增加,适量的噪声有利于多元信号检测,即随机共振存在。在加性高斯噪声强度不变的情况下,当信号是阈上时,错误检测概率随着乘性噪声强度的增加而单调增加,噪声总是恶化信号检测性能;当信号是阈下时,错误检测概率随着乘性噪声强度的增加而单调下降并最终趋于稳定。这些结论有助于噪声改善多元信号检测。
Abstract:
 Stochastic Resonance ( SR) is discussed in nonlinear multiple signal detection for additive noise and multiplicative noise based on the Maximum Posterior Probability ( MAP) criterion in the probability of detection error measure. In the case that multiplicative noise intensity is fixed,when the signal is suprathreshold,the probability of detection error increases monotonously with the additive Gaussian noise intensity and noise always interfere signal detection;when the signal is subthreshold,with the increase of additive Gaussian noise in-tensity,the probability of detection error gradually reduces to a minimum value and then increases slowly and the noise can improve the signal detection,i. e. ,SR exists. In the case that additive noise intensity is fixed,when the signal is suprathreshold,the probability of de-tection error increases monotonously with the multiplicative noise intensity which indicates that noise is always deteriorate signal detection performance;when the signal is subthreshold,the probability of detection error decreases monotonously and finally tends to a steady value with the increasing of multiplicative noise intensity. These conclusions can be able to be helpful for noise improving multiple signal detec-tion.

相似文献/References:

[1]邱勤伟 焦贤发 朱良燕.对称二值噪声下线性系统的随机共振[J].计算机技术与发展,2010,(08):239.
 QIU Qin-wei,JIAO Xian-fa,ZHU Liang-yan.Stochastic Resonance in Linear System with Dichotomous Noise[J].,2010,(10):239.
[2]王友国 刘沁雨.多阈值系统中高斯混合噪声改善信息的传输[J].计算机技术与发展,2011,(04):120.
 WANG You-guo,LIU Qin-yu.Gaussian Mixture Noise to Improve Information Transmission in Multi-threshold System[J].,2011,(10):120.
[3]张志宏,吴庆波,邵立松,等.基于飞腾平台TOE协议栈的设计与实现[J].计算机技术与发展,2014,24(07):1.
 ZHANG Zhi-hong,WU Qing-bo,SHAO Li-song,et al. Design and Implementation of TCP/IP Offload Engine Protocol Stack Based on FT Platform[J].,2014,24(10):1.
[4]梁文快,李毅. 改进的基因表达算法对航班优化排序问题研究[J].计算机技术与发展,2014,24(07):5.
 LIANG Wen-kuai,LI Yi. Research on Optimization of Flight Scheduling Problem Based on Improved Gene Expression Algorithm[J].,2014,24(10):5.
[5]黄静,王枫,谢志新,等. EAST文档管理系统的设计与实现[J].计算机技术与发展,2014,24(07):13.
 HUANG Jing,WANG Feng,XIE Zhi-xin,et al. Design and Implementation of EAST Document Management System[J].,2014,24(10):13.
[6]侯善江[],张代远[][][]. 基于样条权函数神经网络P2P流量识别方法[J].计算机技术与发展,2014,24(07):21.
 HOU Shan-jiang[],ZHANG Dai-yuan[][][]. P2P Traffic Identification Based on Spline Weight Function Neural Network[J].,2014,24(10):21.
[7]李璨,耿国华,李康,等. 一种基于三维模型的文物碎片线图生成方法[J].计算机技术与发展,2014,24(07):25.
 LI Can,GENG Guo-hua,LI Kang,et al. A Method of Obtaining Cultural Debris’ s Line Chart Based on Three-dimensional Model[J].,2014,24(10):25.
[8]翁鹤,皮德常. 混沌RBF神经网络异常检测算法[J].计算机技术与发展,2014,24(07):29.
 WENG He,PI De-chang. Chaotic RBF Neural Network Anomaly Detection Algorithm[J].,2014,24(10):29.
[9]刘茜[],荆晓远[],李文倩[],等. 基于流形学习的正交稀疏保留投影[J].计算机技术与发展,2014,24(07):34.
 LIU Qian[],JING Xiao-yuan[,LI Wen-qian[],et al. Orthogonal Sparsity Preserving Projections Based on Manifold Learning[J].,2014,24(10):34.
[10]尚福华,李想,巩淼. 基于模糊框架-产生式知识表示及推理研究[J].计算机技术与发展,2014,24(07):38.
 SHANG Fu-hua,LI Xiang,GONG Miao. Research on Knowledge Representation and Inference Based on Fuzzy Framework-production[J].,2014,24(10):38.
[11]翟其清,王友国,郑克. 噪声改善码元传输[J].计算机技术与发展,2014,24(11):152.
 ZHAI Qi-qing,WANG You-guo,ZHENG Ke. Noise Improving Code Elements Transmission[J].,2014,24(10):152.

更新日期/Last Update: 2016-11-29