摘要
针对传统的空时二维参数估计计算复杂、鲁棒性及通用性差、收敛速度慢等缺点,根据空时具有等效性,以空域和时域处理算法可以相互转化为基础,推导出合适的适应度函数,运用改进的粒子群算法同时搜索信号的到达角和频率,用K-means聚类算法对搜索结果进行分类,利用粒子群算法计算简单、全局收敛、可并行性等特点提高算法的搜索能力。计算机仿真表明,与传统的方法相比该算法具有较好的统计和收敛性能。
The traditional space-time two-dimensional parameter estimation has many shortcomings, such as high computational complexity, poor robustness and generalization, and slow convergence speed. According to the space-time equivalence and that the spatial and time domain processing algorithms can be transformed into each other, a suitable fitness function was derived, the improved particle swarm algorithm was used to search the arrival angle and frequency of signal, and the search results were classified with K-means clustering algorithm. Using particle swarm algorithm＇s feature, such as global convergence, parallelism, can improve the algorithm＇s searching capabilities. The computer simulation shows that the proposed method has better statistics and convergence performance than traditional methods.
出处
《计算机应用》
CSCD
北大核心
2012年第11期3054-3056,共3页
journal of Computer Applications
基金
国家自然科学基金资助项目(60971130)
关键词
空时二维参数估计
粒子群算法
谱估计
space-time two-dimensional parameter estimation
Particle Swarm Optimization （PSO）
spectrum estimation
作者简介
通信作者电子邮箱tonggong0412@163．com邱新建（1984-），男，陕西南郑人，博士研究生，主要研究方向：智能信号处理；
山拜·达拉拜（1959-），男（哈萨克族），新疆乌鲁木齐人，教授，博士，主要研究方向：阵列信号处理、智能信号处理；
薛凤凤（1985-），女，陕西西安人，博士研究生，主要研究方向：智能信号处理。
