摘要
粒子滤波算法(PF)中,序列重要性采样引起采样点贫化,进一步经过重采样后造成分集度损失.由于调制星座上信号点的有限离散性,当PF应用于多输入多输出(MIMO)系统解调时,分集度损失将引起解调性能下降.因此,提出首先采用噪声增强的方法来增加重要性采样得到的不同粒子数,从而减缓采样点贫化.然后引入部分确定性重分配结构(PDR)取代重采样过程,以提高具有小重要性权值的粒子被保存的概率,降低分集度损失.分析和仿真结果表明,提出的噪声增强和PDR方法能够显著改善MIMO系统的比特错误性能.
In particle filters （PF）, sequential importance sampling will result in sample impoverishment and further the loss of diversity after resampling. Note that the signal points of modulation constellation are discrete and finite. The loss of diversity will result in performance degradation when PF is utilized to demodulate the multi-input multi-output （MIMO） systems. Therefore, an artificially noise enhancement （ANE） method is first introduced in importance sampling to increase the number of different particles, which will alleviate the sample impoverishment. Second, the partially deterministic reallocation （PDR） scheme is introduced to replace the resampling. The PDR scheme can increase the surviving probability of particles with small importance weights, thereby decreasing the loss of diversity. Analyses and simulation results show that the proposed method can significantly improve the bit error performance of MIMO systems.
出处
《西安电子科技大学学报》
EI
CAS
CSCD
北大核心
2008年第2期237-241,253,共6页
Journal of Xidian University
基金
国家自然科学基金资助项目(60472098
60502046)
重庆市/信息产业部移动通信技术重点实验室开放课题基金
关键词
多输入多输出
粒子滤波
采样点贫化
分集度损失
multi-input multi-output
particle filters
sample impoverishment
loss of diversity
