基于二维高斯样条函数的水下重力被动定位

目前利用水下地理信息对惯导误差进行校正多以各类匹配算法为核心,对此论文提出一种基于二维高斯样条函数的水下重力被动定位新模式.文章首先介绍了一种利用二维高斯样条函数逼近的连续局部重力场模型,随后在该模型的基础上将实测重力表示为连续的解析形式,最后以实测重力作为包含目标位置信息的量测值,并结合扩展卡尔曼滤波算法对目标位置进行最优估计.这种方法无需使用常用的匹配算法,因而也就摆脱了匹配算法的诸多限制.以分辨率为2＆#39;＆#215;2＆#39;的某区域重力异常数据为背景场进行仿真,最终的仿真结果表明：经高斯样条函数逼近的局部重力场模型平均误差小于0.19mGal,水下平均经、纬定位误差分别小于0.59和0.74海里.

Underwater Gravity Passive Location Based on the 2-D Gauss Spline Function

As varies of matching algorithms have been used as the major methods for correcting the errors o{ INS on un- derwater geographic information, a new pattern of passive location which is based on 2-D Gauss spline function is given in this paper. Firstly, the algorithm to reconstruct the local grid gravity base-map with the 2-D Gauss spline function is intro- duced, then the measured gravity is expressed as continual analytical equation. Finally, the classical extended Kalman filter is introduced to estimate the target＇s position with measured gravity as measurement containing the information of target＇s posi- tion. This method can be applied without using any matching algorithms, as a result it is free from the influence of the restriction by using these matching algorithms. Simulation is done on 0. 2′×0. 2′ gravity anomaly database. The simulation result shows that the mean error of analytic equation of local gravity anomalies is less than 0. 19mCJal, and she mean location errors in longitude and latitude are 0. 59 and 0. 74 sea mile respectively.