摘要
针对射频流信号,提出了一种基于卡尔曼滤波的流信号处理压缩感知方法,该方法采用AIC结构采集信号,通过固定长度的观测窗口对信号流进行观测,分析建立系统观测方程和前后窗信号间的状态转移方程,再利用CS算法寻找支撑集与降阶卡尔曼滤波迭代求解相结合求得精确解,并采用子空间追踪算法依次增加新的支撑集来跟踪信号支撑集的变化。实验结果表明,该方法在处理射频信号时有很好的重构效果,重构的均方误差能很快地收敛到已知支撑集的理想卡尔曼滤波结果,且算法的时间复杂度较低。
A compressed sensing of streaming signal based on Kalman Filtering algorithm is proposed for reconstruction of RF streaming signal.The proposed algorithm take Analog Information Converter(AIC)structure to sampling signal.The signal is observed from a sliding window of fixed-length.The system observation equation and the state-space form of two continues windows are established.The exact solution is obtained via using CS algorithm to look for support set and Kalman Filtering iteration.The subspace pursuit algorithm is used to track the change of support set and add new support set in iteration.The experiment results show that the proposed algorithm has better recovery performance in RF signal reconstruction.The reconstructed MSE can quickly converge to the ideal Kalman Filter with low complexity.
出处
《工业控制计算机》
2018年第2期34-35,38,共3页
Industrial Control Computer
关键词
压缩感知
卡尔曼滤波
射频信号
重构流信号
compressed sensing
kalman filtered
RF fingerprinting
rebuilding streaming signal
