A Reconstruction Method Based on AL0FGD for Compressed Sensing in Border Monitoring WSN System

In this paper, to monitor the border in real-time with high efficiency and accuracy, we applied the compressed sensing (CS) technology on the border monitoring wireless sensor network (WSN) system and proposed a reconstruction method based on approximately l0 norm and fast gradient descent (AL0FGD) for CS. In the frontend of the system, the measurement matrix was used to sense the border information in a compressed manner, and then the proposed reconstruction method was applied to recover the border information at the monitoring terminal. To evaluate the performance of the proposed method, the helicopter sound signal was used as an example in the experimental simulation, and three other typical reconstruction algorithms 1)split Bregman algorithm, 2)iterative shrinkage algorithm, and 3)smoothed approximate l0 norm (SL0), were employed for comparison. The experimental results showed that the proposed method has a better performance in recovering the helicopter sound signal in most cases, which could be used as a basis for further study of the border monitoring WSN system.为实现高效精准的实时边境监控，本文将压缩感知（compressed sensing, CS）技术应用于边境监控无线传感器网络（wireless sensor network, WSN）系统，并提出了一种基于近似l0范数与快速梯度下降（AL0FGD）的压缩感知重构方法。在系统前端，通过测量矩阵以压缩方式感知边境信息，随后将所提重构方法应用于监控终端以恢复边境信号。为验证所提方法的性能，本文以直升机声信号为例开展实验仿真，并选取三种典型重构算法进行对比：1）分裂Bregman算法；2）迭代收缩算法；3）平滑近似l0范数（SL0）算法。实验结果表明，在多数场景下所提方法在直升机声信号重构任务中表现更优，可为边境监控无线传感器网络系统的后续研究提供参考依据。