利用部分采样的数字混合信号单通道盲分离算法
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国防科技大学信息通信学院,西安,710106

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国家自然科学基金 61701535┫资助项目 国家自然科学基金(61701535)资助项目。


Single Channel Blind Separation Algorithm of Digital Mixed signals Using Partial Sampling
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Institute of Information Communication,National University of Defense Technology,Xi’an, 710106, China

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    摘要:

    针对粒子滤波在数字混合信号单通道盲分离中存在复杂度高的问题,提出一种低复杂度的盲分离算法。分析了算法复杂度高的原因在于符号粒子采样过程中搜索状态空间数与平滑长度成指数倍关系增长。构建了两种采样 方法 部分采样法和混合采样法。前者通过产生若干组序列,使用其代替对所有状态空间进行搜索,利用相应的增量权重完成粒子抽样和权重更新;后者将平滑区间分为两部分,第一部分采用传统的全状态空间法进行搜索,第二部分采用部分采样法进行搜索,利用两个子区间的增量权值完成粒子抽样和权重更新。理论分析和仿真实验结果表明所提算法能有效地降低粒子的搜索空间数。

    Abstract:

    Aiming at the problem of high complexity of particle-filtering in single channel blind separation for digital mixed signals, a novel low complexity algorithm is proposed. By analyzing, the high algorithm complexity lies in the exponentially increase between the number of researching spaces and the smoothing length in particle sampling process. Two sampling schemes are proposed, which are named partial sampling and hybrid sampling. The basic ideal of partial sampling is that instead of exploring the whole space of smoothing interval, several symbol sequences are generated. The incremental weights of each symbol sequences are used for particle sampling and weight update. The hybrid sampling scheme divides the smoothing interval into two parts, in which the first part uses the traditional full space searching scheme and the second one uses the partial sampling scheme. The incremental weights of the two parts are used for particle sampling and weight update. Theoretical analysis and simulation results show that the proposed algorithm can efficiently reduce the particles’ searching spaces.

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马欢,郭勇,吴广恩,闵刚,张长青.利用部分采样的数字混合信号单通道盲分离算法[J].数据采集与处理,2019,34(6):1002-1011

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  • 收稿日期:2018-03-09
  • 最后修改日期:2018-05-27
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  • 在线发布日期: 2019-12-13