基于信噪比的InSAR干涉图自适应滤波

提出了一种基于信噪比的InSAR干涉图相位噪声抑制算法。该算法对Goldstein滤波方法进行了改进，使Goldstein滤波参数 依赖于局部信噪比，从而实现对低信噪比区域进行强滤波、高信噪比区域弱滤波。采用模拟数据和真实数据进行验证，结果表明新算法能有效抑制InSAR干涉图的噪声。

Underwater Gravity Aided Inertial Navigation Based on Local Gravity Field Model

In order to inplementing underwater gravity aided inertial navigation on Kalman filter, the modeling of gravity measurements and their errors is requred. Due to that background a new pattern of gavity aided navigation which is based on local gravity field modeling is given in this paper. In this paper, a fast Fourier series based local gravity field modeling is firstly introduced, and by which the difference between measured gravity and indicated gravity is then expressed as continual analytical equation. After that, the extended Kalman filter is introduced, with the gravity difference used as measurement, to optimum estimate the position error of INS. At the end of this paper a simulation is done on gravity anomaly database, and form the results we can see that the mean error of local gravity field modeling is less than 0.1331mGal, and the mean location error in longitude and latitude is 0.1975 and 0.2499 nautical mile respectively.