基于阈值判决的无线传感器网络混沌信号重构

考虑在传感器网络中重构混沌信号的问题.各传感器同时观测一个混沌信号.由于带宽受限,各传感器量化观测信号并将量化后的数据发送到融合中心,该中心使用无先导卡尔曼滤波算法重构观测的混沌信号.为了节省能量,只有观测信号信噪比高于某个阈值的传感器被选中并发送数据.定义代价函数关联重构性能和能量消耗,分析并仿真了代价函数和阈值之间的关系.结果表明在某些情况下部分传感器发送数据更合理.

Abstract：

The problem of reconstructing chaotic signals in wireless sensor networks is considered.All sensors observe a common chaotic signal.Because of the limited bandwidth,the observations are quantized and the quantized data are sent to a fusion center.The fusion center reconstructs the chaotic signals by using the unscented Kalman filtering(UKF)algorithm.To save energy,only the sensors for which the SNR of the observation is higher than a threshold are chosen for transmission.A cost function is defined to relate the reconstruction performance and the energy consumption.The relationship between the cost function and the threshold is analyzed and simulated.The results show that under certain conditions,it is more reasonable that only a portion of the sensors transmit data.     

基于无先导卡尔曼滤波的卷积混沌信号的盲提取

从卷积混合的混沌信号中提取出所需的信号是一个挑战性课题.假设需要提取的混沌信号的映射已知,寻找提取向量的问题就可以转化为非线性系统的状态检测问题.为此,提出了一种基于无先导卡尔曼滤波的盲提取算法.仿真结果证明在无噪声或高信噪比情况下该算法的有效性.     

基于阈值判决的无线传感器网络混沌信号重构

考虑在传感器网络中重构混沌信号的问题.各传感器同时观测一个混沌信号.由于带宽受限,各传感器量化观测信号并将量化后的数据发送到融合中心,该中心使用无先导卡尔曼滤波算法重构观测的混沌信号.为了节省能量,只有观测信号信噪比高于某个阈值的传感器被选中并发送数据.定义代价函数关联重构性能和能量消耗,分析并仿真了代价函数和阈值之间的关系.结果表明在某些情况下部分传感器发送数据更合理.