混沌信号在无线传感器网络中的盲分离  

Blind source separation of chaotic signals in wireless sensor networks

作　　者：黄锦旺[1] 冯久超[1] 吕善翔[1] 

机构地区：[1]华南理工大学电子与信息学院,广州510641

出　　处：《物理学报》2014年第5期44-51,共8页Acta Physica Sinica

基　　金：国家自然科学基金(批准号:60872123;61101014);国家-广东省自然科学基金联合基金(批准号:U0835001);广东省高层次人才项目基金(批准号:N9101070);中央高校基本业务费(批准号:2012ZM0025)资助的课题~~

摘　　要：混沌信号在本质上属于非线性非高斯信号,它在无线传感器网络下的应用还涉及到信号量化问题,这使得混沌信号在此应用环境下的信号盲分离更为棘手.针对此问题,本文在容积卡尔曼粒子滤波的框架下提出一种解决方法.文中首先推导出观测信号的概率密度函数,在量化比特有限的情况下,采用最优量化器,获得最优的量化结果.在此基础上,使用容积卡尔曼滤波器产生粒子滤波中的重要性概率密度函数,融入最新的观测值,提高粒子对系统状态后验概率的逼近,提高信号盲分离的精度.仿真结果表明算法能够有效地分离混合混沌信号,参数估计的精度及其运算量均优于已有的无先导卡尔曼粒子滤波算法,其运行时间为无先导卡尔曼粒子滤波算法的88.77%.Chaotic signal is essentially a nonlinear and non-Gaussian signal, which involves signal quantization when used in wireless sensor networks（WSNs）. It makes the blind source separation of chaotic signal in WSNs more dificult to address. To solve the problem, we propose a new source separation algorithm based on cubature Kalman particle filter（CPF） in this paper. First the probability density function of the observed signal is derived and the optimal quantization is used; this can achieve the optimal quantization of signal under the limited budget of quantization bits. After that,the algorithm uses cubature Kalman filter（CKF） to generate the important proposal distribution of the particle filter（PF）, integrating the latest observation and improving the approximation to the system posterior distribution, which will improve the performance of the signal separation. Simulation results show that the algorithm can separate mixed chaotic signal efiectively, it is superior over the unscented Kalman particle filter（UPF） counterpart in accuracy and computation overhead. The running time is 88.77% compared to the UPF counterpart.

关 键 词：混沌信号 盲分离 无线传感器网络 粒子滤波器 

分 类 号：TP212.91[自动化与计算机技术—检测技术与自动化装置]