噪音(视频)
补偿(心理学)
摇摆
噪声测量
计算机科学
物理
残余物
作者
Diankun Zhang,Xiaojun Liu,Xiaodong Qu,Wanhua Zhu,Ling Huang,Guangyou Fang
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing
[Institute of Electrical and Electronics Engineers]
日期:2021-07-20
标识
DOI:10.1109/tgrs.2021.3095564
摘要
Aeromagnetic noise compensation is a vital part of aerial survey measurement, and its compensation effect directly determines the quality of aeromagnetic survey data. At present, the commonly used compensation model is the T-L model, and the least squares method is used to solve for the coefficients. However, the noise source modeled in the T-L model is incomplete. Since the tail boom cannot be completely rigid, tail-boom swing is an unavoidable problem in aeromagnetic measurement. This kind of swing is the most obvious when the aircraft is maneuvering, and it will significantly interfere with the measurement data of the sensor. In this article, two causes of the swing noise are analyzed, and the nonlinear relationship between the swing displacement and the noise is derived. Since it is difficult to express the nonlinear relationship with mathematical forms to compensate for the aeromagnetic data, we propose a new compensation method that uses a 1-D convolutional neural network to perform secondary compensation on the data already compensated by the T-L model in order to remove the effect of tail-boom swing. The flight experiment data show that the proposed method can significantly improve the quality of aeromagnetic data. Compared with the T-L method, the improve ratio is increased by 60%-100%. It shows that the proposed method has a remarkable compensation effect for aeromagnetic noise.
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