计算机技术与发展Issue(11)：59-61,65,4.DOI:10.3969/j.issn.1673-629X.2013.11.015
基于自调节粒子群算法的盲检测
Blind Detection Based on Self-adaptive Particle Swarm Optimization
摘要
Abstract
The blind algorithm based on Particle Swarm Optimization ( PSO) can achieve signal blind detection successfully,but has some defects such as converging to local optimum or slow convergence. By analyzing the performance of the PSO algorithm and the parameter setting,an improved blind algorithm based on self-adjustment PSO algorithm is presented. The thoughts are through blind detection sys-tem model based on MIMO system,translated the blind detection problem into quadratic optimization problem,and the new PSO algo-rithm was used to solve the problem. The experiment results show that the new PSO has good features such as strong global search capa-bility,rapid convergence and short computation time,which confirms the validity and feasibility of this approach.关键词
盲检测/自调节因子/粒子群算法Key words
blind detection/self-adjustment factor/PSO分类
信息技术与安全科学引用本文复制引用
张昀,于舒娟,王京..基于自调节粒子群算法的盲检测[J].计算机技术与发展,2013,(11):59-61,65,4.基金项目
国家自然科学基金资助项目(60772060) （60772060）
南京邮电大学引进人才项目(NY212022) （NY212022）
南京邮电大学青蓝工程项目(NY210037) （NY210037）
