Abstract
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.
Highlights
Border monitoring plays an important role in the national defense
We take the helicopter sound signal as an example to evaluate the performance of the proposed method in border monitoring WSN system
To recover the information more efficiently at the monitoring terminal, we proposed a novel reconstruction method based on AL0FGD for Compressed Sensing (CS)
Summary
Border monitoring plays an important role in the national defense. The traditional border monitoring system consists of security checkpoints and border troops, which suffers from intensive human involvement. As the long stretches of borders and the complexity of the terrain, the difficulty of manual patrolling is increased. To minimize the need for human support and monitor the border in real-time with high accuracy, multiple surveillance technologies, which complement each other, are required [1]. PLOS ONE | DOI:10.1371/journal.pone.0112932 December 2, 2014
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