水下重力异常相关极值匹配算法  

Correlative Extremum Matching Algorithm Using Underwater Gravity Anomalies

作　　者：李姗姗[1] 吴晓平[1] 马彪[1] 

机构地区：[1]信息工程大学测绘学院,河南郑州450052

出　　处：《测绘学报》2011年第4期464-469,476,共7页Acta Geodaetica et Cartographica Sinica

基　　金：国家自然科学基金(40774031);全国优秀博士学位论文作者专项基金(200344)

摘　　要：重力辅助惯性导航是利用地球物理特征信息——重力来修正水下潜器惯性导航误差,其关键技术是匹配算法。基于平均平方差最小准则构造差分降权相关目标函数模型,针对受干扰误差随机影响,基于目标函数模型搜索得到的多个有效位置,均有可能以不同概率密度源于正确位置这一问题,提出概率数据关联滤波重力匹配算法,与最近邻法相比,该算法确定的航迹更接近于真实位置,提高算法的可靠性与抗干扰性;分析探讨序列采样间隔对匹配精度的影响。通过试验区仿真匹配,结果表明,当选择适当的采样间隔与采样长度,该算法能有效修正导航误差。Gravity-aided inertial navigation is a technique that makes use of gravity to correct inertial navigation error of underwater vehicles.Matching algorithm is vital in the process of gravity-aided inertial navigation.An object function model of differential descending weight-correlation was put forward based on the principle of mean square deviation.Because of the influence of random errors,there are multi-available positions determined by the object function model which are around the real position with different probabilities.Then,a probabilistic data association filter（PDAF） algorithm was proposed.Navigation position determined by PDAF is much closer to the actual one than that chosen by the nearest neighbor method,the reliability and the anti-jamming of the algorithm are improved.Then,the effect of the serial sampling intervals on the matching precision was discussed.Through simulations in the experimental area,the results showed that navigation errors were corrected effectively with proper sampling intervals and sampling lengths based on the algorithm.

关 键 词：重力异常 重力辅助惯性导航 相关极值匹配 差分降权相关 概率数据关联 采样间隔 

分 类 号：P223[天文地球—大地测量学与测量工程]