Compressed Sensing Based Adaptive-Resolution Data Recovery in Underwater Sensor Networks
Compressed Sensing Based Adaptive-Resolution Data Recovery in Underwater Sensor Networks
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DOI：
10.12720/jcm.11.3.317-324
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Wen-jing Kang;Gongliang Liu;B. Hu
中科院分区：
文献类型：
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作者：
Wen-jing Kang;Gongliang Liu;B. Hu
随着海洋开发的蓬勃发展，水声传感器网络已成为探索和监测海洋的重要方法。针对水下环境的恶劣性，提出了一种基于压缩感知理论的自适应分辨率数据恢复方案。该方案的基本思想是在牺牲数据分辨率的情况下获得更好的恢复数据质量。首先提出了数据分辨率调整方法，然后提出了恢复数据质量评价算法和自适应分辨率选择策略。实验结果表明，该方案能够准确地评估恢复数据的质量，并自适应地选择分辨率。在保证数据分辨率的前提下，成功地对恢复分辨率进行了修正，以达到更高的精度。
—With marine development thriving today, underwater acoustic sensor networks have become a vital method in exploring and monitoring the ocean. In this paper, a data recovery scheme with adaptive resolution based on compressed sensing theory is proposed, aiming at acclimating to the atrocious conditions under the water. The fundamental thought of the scheme is to achieve better quality of recovered data at the sacrifice of data resolution. Data resolution adjusting method is raised firstly, then a recovered data quality evaluation algorithm and an adaptive resolution selecting strategy are proposed. The experimental results show that schemes presented are able to evaluate the quality of the recovered data accurately and adaptively select the resolution. Accordingly, recovery resolution is modified triumphantly to achieve higher accuracy on the premise of acceptable data resolution.