摘要:
基于Gabor框架的窄脉冲信号采样及重构效果已经得到验证,其解决了有限新息率(finite rate of innovation, FRI)采样方法无法在波形未知的情况下重构出脉冲波形的问题。但是目前的Gabor框架采样系统的窗函数构造复杂且难以物理实现。本文将指数再生窗函数引入Gabor框架,将窗函数序列调制部分简化为一阶巴特沃斯模拟滤波器,构造了Gabor系数重构所需要的压缩感知(compressed sensing, CS)测量矩阵。为了使得测量矩阵满足信号精确重构所需的约束等距特性(restricted isometry property, RIP),根据高阶指数样条函数能量聚集特性,选择了最优的窗函数支撑宽度,推导了信号重构所需的约束条件,还对其鲁棒性进行了分析。本文通过仿真实验对上述分析进行了有效验证,该系统可应用于测试仪器、状态监测、雷达及通信领域等多种背景下的窄脉冲信号采样与重构。
Abstract:
Sampling and reconstruction of short pulses based on Gabor frames have been proved to be effective, which overcome the di?culties that finite rate of innovation (FRI) sampling is unable to reconstruct the pulse streams without the prior information of waveforms. However, the windows sequences of sampling scheme based on Gabor frames proposed at present show complicated structure and are hard to realize physically. The exponential reproducing windows are then introduced in this paper and the windows sequences can be simplified as a first-order analog Butterworth filter. At the same time, the compressed sensing (CS) measurement matrix is constructed for the recovery of Gabor coe?cients. In order to satisfy the restricted isometry property (RIP) of the measurement matrices for perfect signal reconstruction, we select appropriate windows for support according to the energy accumulation property. A restricted condition is deduced for perfecting the signal reconstruction and the system robustness is analyzed. By numerical simulations the above analysis is verified. This novel scheme can be used to implement short pulses sampling and reconstruction in the field of instrumentation, condition monitoring, radar and the communication.
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