HUANG Lei,LI Xiaopeng,et al.Compressed sampling technologies for UAV data backhaul: opportunities and challenges[J].Journal of Shenzhen University Science and Engineering,2019,36(5):473-481.[doi:10.3724/SP.J.1249.2019.05473]





Compressed sampling technologies for UAV data backhaul: opportunities and challenges
1)深圳大学电子与信息工程学院,广东深圳 518060;2)广东省(深圳大学-达实智能)位置感知与探测工程技术研究中心,广东深圳 518060
HUANG Lei1 2 LI Xiaopeng1 2 HUANG Min1 2 LI Qiang1 2 ZHAO Bo1 2 SUN Weize1 2 and ZHANG Peichang1 2
1) College of Electronics and Information Engineering, Shenzhen University, Shenzhen 518060, Guangdong Province, P.R.China
2) Guangdong Provincial (SZU-DAS) Positioning & Sensing Engineering Technology Research Center, Shenzhen University, Shenzhen 518060, Guangdong Province, P.R.China
无人机压缩采样带宽受限数据回传图像处理 1-bit 压缩采样 矩阵补全
unmanned aerial vehicle (UAV) compressed sampling limited bandwidth data backhaul image processing one-bit compressed sampling matrix completion
随着无人机(unmanned aerial vehicle, UAV)的广泛应用,其与地面接收站实时共享机载传感器数据成为工业界的迫切需求.然而,目前为无人机开放使用的频谱资源稀缺,通信信道带宽非常有限,这就促使人们研究如何在带宽受限条件下实现无人机数据的实时无损回传.作为突破经典奈奎斯特(Nyquist)采样理论的新技术,压缩采样将是解决这类问题的最佳方案.本文通过对比当前无人机通信技术核心参数,揭示现有通信技术标准无法满足无人机数据通信对信道带宽日益迫切的需求,评述无人机数据回传压缩技术,对压缩感知、1-bit压缩采样、相位恢复和矩阵补全技术原理进行回顾.采用压缩感知和矩阵补全技术对实测数据进行验证,结果表明,压缩感知和矩阵补全技术可在带宽不变的情况下,显著降低数据的传输时间.最后提出无人机数据压缩和恢复领域的4个研究发展方向.
With the widespread application of unmanned aerial vehicle (UAV), the real-time sharing of data between UAV and base station has become an urgent demand in industry. However, the spectrum resources available for UAV data transmission are extremely precious, resulting in rather limited channel bandwidth. This, in turn, motives ones to explore efficient technologies for real-time non-destructive backhaul of drone data under bandwidth-constrained conditions. As a new technology breaking through the classical Nyquist sampling theorem, the compressive sampling (CS) turns out to be a promising solution to the aforementioned problem. By comparing the core parameters of current UAV communication technologies, this paper reveals that the existing standards of communications cannot meet the increasing requirements of UAV data transmission. Subsequently, four representative CS techniques, including compressed sensing, one-bit compressed sampling, phase retrieval and matrix completion, are briefly reviewed. Then, the simulations of compressed sensing and matrix completion technologies with real-world data are carried out to demonstrate the effectiveness which reveals that compressed sensing and matrix completion methods are able to significantly reduce the transmission time of data backhaul without changing the bandwidth. Ultimately, this paper also describes four research and development directions in the field of UAV data compression and recovery.


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Foundation:National Natural Science Foundation of China (U1713217, U1501253)
Corresponding author:Professor HUANG Lei. E-mail:lhuang@szu.edu.cn
Citation:HUANG Lei, LI Xiaopeng, HUANG Min, et al. Compressed sampling technologies for UAV data backhaul: opportunities and challenges [J]. Journal of Shenzhen University Science and Engineering, 2019, 36(5): 473-481.(in Chinese)
基金项目:国家自然科学基金资助项目(U1713217, U1501253)
引文:黄磊,李晓鹏,黄敏,等.面向无人机数据回传的压缩采样技术:机会与挑战[J]. 深圳大学学报理工版,2019,36(5):473-481.
更新日期/Last Update: 2019-09-30