[1]吴尘,徐新洲,王浩,等.基于模拟退火算法的稀疏系数抽取滤波器设计[J].东南大学学报(自然科学版),2015,45(4):631-634.[doi:10.3969/j.issn.1001-0505.2015.04.003]
 Wu Chen,Xu Xinzhou,Wang Hao,et al.Design of sparse coefficient decimation filter using simulated annealing algorithm[J].Journal of Southeast University (Natural Science Edition),2015,45(4):631-634.[doi:10.3969/j.issn.1001-0505.2015.04.003]
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基于模拟退火算法的稀疏系数抽取滤波器设计()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
45
期数:
2015年第4期
页码:
631-634
栏目:
信息与通信工程
出版日期:
2015-07-20

文章信息/Info

Title:
Design of sparse coefficient decimation filter using simulated annealing algorithm
作者:
吴尘1徐新洲1王浩2赵力1
1东南大学水声信号处理教育部重点实验室, 南京210096; 2杭州电子科技大学通信工程学院, 杭州310018
Author(s):
Wu Chen1 Xu Xinzhou1 Wang Hao2 Zhao Li1
1Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education, Southeast University, Nanjing 210096, China
2School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, China
关键词:
系数抽取滤波器 稀疏 模拟退火 线性优化
Keywords:
coefficient decimation filter sparse simulated annealing linear programming
分类号:
TN911.72
DOI:
10.3969/j.issn.1001-0505.2015.04.003
摘要:
为了减少系数抽取滤波器的乘法器个数,提出了一种基于模拟退火算法的稀疏系数抽取滤波器设计方法.该方法将系数抽取滤波器的非凸稀疏设计转化为一个寻找最稀疏的系数抽取滤波器的零系数位置集合的组合优化问题,然后利用模拟退火算法来求解该问题.该方法结合贪婪思想逐步地增加系数抽取滤波器的稀疏度,直到没有更加稀疏的设计结果存在.在每一步中将系数抽取滤波器的稀疏度固定,利用模拟退火算法来寻找满足给定设计标准的系数抽取滤波器的零系数位置集合.实验结果表明,该方法可以有效地减少系数抽取滤波器所需的乘法器个数.
Abstract:
In order to reduce the number of multipliers of the coefficient decimation filter, a design method based on the simulated annealing(SA)algorithm is proposed. The proposed method transforms the non-convex sparse design of coefficient decimation filter into a combinatorial optimization problem which finds the sparsest set of the positions of the zero coefficients, and then uses the simulated annealing algorithm to solve it. Combining the greedy theory, the method successively increases the sparsity of the coefficient decimation filter until no sparser design result exists. At each step of the method, the sparsity of the coefficient decimation filter is fixed, and SA is used for finding the set of the positions of the zero coefficients that satisfies the design specifications. Simulation results demonstrate that the proposed method can effectively reduce the number of multipliers of the coefficient decimation filter.

参考文献/References:

[1] Mahesh R, Vinod A P. Coefficient decimation approach for realizing reconfigurable finite impulse response filters [C]//IEEE International Symposium on Circuits and Systems. Seattle, USA, 2008: 81-84.
[2] Mahesh R, Vinod A P. Low complexity flexible filter banks for uniform and non-uniform channelisation in software radios using coefficient decimation [J]. IET Circuits, Devices & Systems, 2011, 5(3): 232-242.
[3] Lin M, Vinod A P, See C M S. A new flexible filter bank for low complexity spectrum sensing in cognitive radios [J]. Journal of Signal Processing Systems, 2011, 62(2): 205-215.
[4] Smitha K G, Vinod A P. A new low power reconfigurable decimation-interpolation and masking based filter architecture for channel adaptation in cognitive radio handsets [J]. Physical Communication, 2009, 2(1/2): 47-57.
[5] Parks T, McClellan J. Chebyshev approximation for nonrecursive digital filters with linear phase [J]. IEEE Transactions on Circuit Theory, 1972, 19(2): 189-194.
[6] Sheikh Z U, Gustafsson O. Linear programming design of coefficient decimation FIR filters [J]. IEEE Transactions on Circuits and Systems Ⅱ: Express Briefs, 2012, 59(1): 60-64.
[7] Donoho D L. Compressed sensing [J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.
[8] Baran T, Wei D, Oppenheim A V. Linear programming algorithms for sparse filter design [J]. IEEE Transactions on Signal Processing, 2010, 58(3): 1605-1617.
[9] Wu C, Zhang Y, Shi Y, et al. Sparse FIR filter design using binary particle swarm optimization[J]. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 2014, 97(12): 2653-2657.
[10] Kirkpatrick S, Gelatt C D, Vecchi M P. Optimization by simulated annealing [J]. Science, 1983, 220(4598): 671-680.
[11] Du X P, Cheng L Z, Chen D Q. A simulated annealing algorithm for sparse recovery by l0 minimization [J]. Neurocomputing, 2014, 131: 98-104.
[12] Xu F M, Wang S H. A hybrid simulated annealing thresholding algorithm for compressed sensing [J]. Signal Processing, 2013, 93(6): 1577-1585.

备注/Memo

备注/Memo:
收稿日期: 2015-01-07.
作者简介: 吴尘(1987—),男,博士生;赵力(联系人),男,博士,教授,博士生导师,zhaoli@seu.edu.cn.
基金项目: 国家自然科学基金资助项目(61231002,61273266,61375028)、教育部博士点专项基金资助项目(20110092130004).
引用本文: 吴尘,徐新洲,王浩,等.基于模拟退火算法的稀疏系数抽取滤波器设计[J].东南大学学报:自然科学版,2015,45(4):631-634. [doi:10.3969/j.issn.1001-0505.2015.04.003]
更新日期/Last Update: 2015-07-20