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:2019,28(9):196-202
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基于GA-IPF的PCMA信号盲分离算法
张珊珊1,2, 陈刚1,2, 鲁华祥1,2,3,4, 邓琪1,2
(1.中国科学院大学, 北京 100049;2.中国科学院 半导体研究所, 北京 100083;3.中国科学院 脑科学与智能技术卓越创新中心, 上海 200031;4.半导体神经网络智能感知与计算技术北京市重点实验室, 北京 100083)
Blind Separation of PCMA Signals Based on GA-IPF
(1.University of Chinese Academy of Sciences, Beijing 100049, China;2.Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China;3.Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China;4.Semiconductor Neural Network Intelligent Perception and Computing Technology Beijing Key Lab, Beijing 100083, China)
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投稿时间:2019-03-05    修订日期:2019-04-02
中文摘要: 针对非合作接收PCMA信号盲分离问题,提出一种遗传改进粒子滤波算法(Improved Particle Filtering based on Genetic Algorithm,GA-IPF).该算法以粒子滤波的算法框架为基础,建立多个状态空间分布以逼近真实后验概率密度;同时引入遗传算法替代重采样产生新粒子,增加粒子多样性,避免了重采样过程中的粒子耗尽问题.仿真实验表明,该算法载噪比为9 dB时,分离准确率达到95%,与QRD-M Gibbs等算法相比,信号捕获能力提高4 dB,且算法复杂度降低近60%.
中文关键词: PCMA  状态空间分布  粒子滤波  遗传算法
Abstract:Aiming at the blind separation problem of non-cooperative receiving PCMA signals, Improved Particle Filtering based on Genetic Algorithm (GA-IPF) is proposed. Based on the particle filter algorithm framework, the algorithm establishes multiple state distributions to approximate the true posterior probability density. At the same time, genetic algorithm is introduced instead of resampling to generate new particles, which increases particle diversity and avoids particle depletion during resampling. Simulation results show that when the carrier-to-noise ratio is greater than 9 dB, the separation accuracy is over 95%, compared with QRD-M Gibbs and other algorithms, the signal acquisition capability of the algorithm is improved by 4 dB, and the algorithm complexity is reduced by nearly 60%.
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基金项目:中国科学院战略性先导科技专项(A类)(XDA18040400);中国科学院国防科技创新基金项目(CXJJ-17-M152);国家自然科学基金(61701473);北京市科技计划项目(Z181100001518006);中国科学院STS计划(KFJ-STS-ZDTP-070)
引用文本:
张珊珊,陈刚,鲁华祥,邓琪.基于GA-IPF的PCMA信号盲分离算法.计算机系统应用,2019,28(9):196-202
ZHANG Shan-Shan,CHEN Gang,LU Hua-Xiang,DENG Qi.Blind Separation of PCMA Signals Based on GA-IPF.COMPUTER SYSTEMS APPLICATIONS,2019,28(9):196-202

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