针对强电磁干扰极易掩盖微弱的大地电磁有用信号,本文结合奇异值分解在去噪方面的优越性,提出基于自适应多分辨率奇异值分解(Adaptive Multi-Resolution Singular Value Decomposition,AMRSVD)的大地电磁数据处理方法.首先对大地电磁数据构建Hankel矩阵,利用MRSVD得到不同分辨率的近似信号和细节信号;然后选用近似信号和细节信号的标准差差值,对大地电磁数据进行信噪辨识;接着结合MRSVD和相邻细节信号的标准差差值,提出先验信息未知情况下的AMRSVD法;最后对辨识出的强干扰运用AMRSVD去除噪声,重构有用信号.实验结果表明,该方法的处理效率高,能有效分离出相关性较强的噪声,时间序列和视电阻率-相位曲线均得到有效改善.
In collaboration with 12 other institutions, the Meteorological Observation Center of the China Meteorological Administration undertook a comprehensive marine observation experiment in the South China Sea using the Yilong-10 high-altitude large unmanned aerial vehicle(UAV). The Yilong-10 UAV carried a self-developed dropsonde system and a millimeter-wave cloud radar system. In addition, a solar-powered unmanned surface vessel and two drifting buoys were used. The experiment was further supported by an intelligent, reciprocating horizontal drifting radiosonde system that was deployed from the Sansha Meteorological Observing Station, with the intent of producing a stereoscopic observation over the South China Sea. Comprehensive three-dimensional observations were collected using the system from 31 July to2 August, 2020. This information was used to investigate the formation and development processes of Typhoon Sinlaku(2020). The data contain measurements of 21 oceanic and meteorological parameters acquired by the five devices, along with video footage from the UAV. The data proved very helpful in determining the actual location and intensity of Typhoon Sinlaku(2020). The experiment demonstrates the feasibility of using a high-altitude, large UAV to fill in the gaps between operational meteorological observations of marine areas and typhoons near China, and marks a milestone for the use of such data for analyzing the structure and impact of a typhoon in the South China Sea. It also demonstrates the potential for establishing operational UAV meteorological observing systems in the future, and the assimilation of such data into numerical weather prediction models. 相似文献
针对执行水质监测任务过程中固定浮标监测站单点监测存在局限性、船载观测人员取样耗时耗力等问题,本文设计了一种搭载多点、分层自动采水取样装置的智能无人船水质监测系统,可实现目标水域的多点、分层连续水质数据测量及取样。该智能无人船具备基于快速随机树(Rapid Random Tree)算法的自主避障和快速路径规划功能,解决了现有无人船技术存在的多障碍自主路径规划难等问题。同时,本设计结合了ARM9控制芯片、M5310无线传输模块,通过可视化的显示界面和远程WEB访问的功能,大大提升了科研人员及时处理特殊情况便捷性。通过实验测试及比对分析,证明本设计具有智能高效、稳定可靠等优点,能够满足职能部门对于水质监测工作的需要。 相似文献