| 引入最优自适应因子的状态模型法航空重力测量 |
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| 引用本文： | 闫文林,李英成,薛艳丽.引入最优自适应因子的状态模型法航空重力测量[J].地球物理学报,2016,59(4):1267-1274. |
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| 作者姓名： | 闫文林 李英成 薛艳丽 |
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| 作者单位： | 1. 中测新图(北京)遥感技术有限责任公司, 北京 100039;2. 山东大学空间科学研究院, 山东威海 264209;3. 中国测绘科学研究院, 北京 100830 |
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| 基金项目： | 航空遥感技术国家测绘地理信息局重点实验室开放研究课题(航空遥感的POS数据在航空重力测量中的应用),国家自然科学基金资助项目(41274042)和国家自然科学基金资助项目(41504032)联合资助. |
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| 摘 要： | 引入最优自适应比例因子以改善状态模型法航空重力测量的精度,并尝试将其应用到我国困难地区的重力测量.把重力扰动当作状态量引入Kalman滤波进行最优估计,并引入最优自适应因子调节状态信息的权阵,提高重力扰动的最终解算精度.利用新疆地区不同航次和航高的实测数据,计算了垂直向下方向上的重力扰动.与全球重力场模型EGM2008的对比分析表明,差值中误差在10mGal左右,接近国家在困难地区重力测量精度的限差要求.
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| 关 键 词： | 航空重力测量 状态模型法 Kalman滤波 自适应因子 |
| 收稿时间： | 2014-09-11 |
Introducing the optimal adaptive factor to improve the accuracy of airborne gravimetry based on the state model method |
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| YAN Wen-Lin,LI Ying-Cheng,XUE Yan-Li.Introducing the optimal adaptive factor to improve the accuracy of airborne gravimetry based on the state model method[J].Chinese Journal of Geophysics,2016,59(4):1267-1274. |
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| Authors: | YAN Wen-Lin LI Ying-Cheng XUE Yan-Li |
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| Institution: | 1. China TopRS Technology, Beijing 100039, China;2. Institute of Space Sciences, Shandong University, Shandong Weihai 264209, China;3. Chinese Academy of Surveying and Mapping, Beijing 100830, China |
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| Abstract: | This work introduced the optimal adaptive factor to improve the accuracy of airborne gravimetry based on the state model method, attempted to apply this technique to areas of China where gravity measurement is difficult. Gravity disturbance was used as the state vector of Kalman Filter, and the optimal adaptive factor was introduced to adjust the weight matrix of the state information to improve the accuracy of estimation of the gravity disturbance. Based on flight data at different times and different heights in Xinjiang, this work calculated the vertical gravity disturbances, and compared them with the EGM2008 global gravity model. The differences between the results from the proposed method and the EGM2008 model are around 10 mGal, which is close to the precision demand of national gravity surveys in difficult areas. |
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| Keywords: | Airborne gravimetry State model method Kalman Filter Adaptive factor |
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