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.
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.
出处
《应用科技》
CAS
2013年第3期35-38,共4页
Applied Science and Technology
基金
青年科学基金资助项目(61101141)
关键词
粒子滤波
信道估计
智能算法
人工蜂群
非高斯噪声
particle filter
channel estimation
intelligent algorithm
artificial bee colony
non-Gaussian noise
