[1]彭涛,李一兵,孙志国.人工蜂群粒子滤波信道估计算法研究[J].应用科技,2013,40(03):35-38.[doi:10.3969/j.issn.1009-671X.201303014]
 PENG Tao,LI Yibing,SUN Zhiguo.Research of channel estimation algorithm based on artificial bee colony particle filtering[J].Applied science and technology,2013,40(03):35-38.[doi:10.3969/j.issn.1009-671X.201303014]
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人工蜂群粒子滤波信道估计算法研究(/HTML)
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《应用科技》[ISSN:1009-671X/CN:23-1191/U]

卷:
第40卷
期数:
2013年03期
页码:
35-38
栏目:
计算机技术与应用
出版日期:
2013-06-05

文章信息/Info

Title:
Research of channel estimation algorithm based on artificial bee colony particle filtering
文章编号:
1009-671X(2013)03-0035-04
作者:
彭涛1李一兵2孙志国2
1. 海军驻哈尔滨地区舰船配套军代表室,黑龙江 哈尔滨 150001
2. 哈尔滨工程大学 信息与通信工程学院,黑龙江 哈尔滨 150001
Author(s):
PENG Tao1 LI Yibing2 SUN Zhiguo2
1. Navy Ship Support Representative Office Stationed in Harbin, Harbin 150001, China
2. College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
关键词:
粒子滤波信道估计智能算法人工蜂群非高斯噪声
Keywords:
particle filter channel estimation intelligent algorithm artificial bee colony non-Gaussian noise
分类号:
TN92
DOI:
10.3969/j.issn.1009-671X.201303014
文献标志码:
A
摘要:
人工蜂群算法是用以解决复杂优化问题的新方法,具有收敛速度快、优化性能高等特点. 将人工蜂群算法与粒子滤波相结合应用于信道估计可以摆脱常规方法对线性高斯条件的束缚,具有理论依据和现实意义. 结合2种算法的优势提出了人工蜂群粒子滤波,采用人工蜂群算法确定粒子滤波的建议分布. 仿真将Alpha稳定分布作为非高斯噪声模型,实现了粒子滤波及其改进算法的信道估计研究. 结果表明人工蜂群算法与其他智能算法相比具有更快的收敛速度,改进人工蜂群粒子滤波与无迹粒子滤波相比极大地提高了信道估计精度.
Abstract:
The artificial bee colony algorithm is a new method to solve complex optimization problems, with the advantages of faster convergence and higher optimization performance. Channel estimation which is realized by combining artificial bee colony algorithm with particle filter can get rid of the shackles of the conventional method to linear Gaussian conditions, having a theoretical basis and practical significance. Combined with the advantages of the two algorithms, this paper proposes artificial bee colony particle filter, so that the proposal distribution is determined by artificial bee colony algorithm. The channel estimation simulation of particle filtering and its improved algorithm are achieved based on Alpha stable distribution which is used as a non-Gaussian noise model. The simulation results show that artificial bee colony algorithm has faster convergence speed comparing with other intelligent algorithms, and the improved artificial bee colony particle filter improves channel estimation accuracy greatly comparing with unscented particle filter.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2013-03-13.
网络出版日期:2013-05-23.
基金项目:青年科学基金资助项目(61101141).
作者简介:彭涛(1979-), 男,工程师,主要研究方向:通信信号处理,
E-mail: hanyafei0704@163.com.
更新日期/Last Update: 2013-06-27