基于捷联惯导的航空重力测量滤波算法

Filtering algorithm of airborne gravimetry based on SINS

在航空重力测量中通常需要采用卡尔曼滤波来对比力测量误差进行估计。针对航空重力测量只需要进行事后处理的特点,提出了两种新方法来提高比力测量的精度:一是最优卡尔曼滤波平滑算法,该算法的估计值是前向/反向卡尔曼滤波器的估计值的最优组合;二是迭代算法,由于在滤波模型中通常不对重力异常进行建模,而模型误差的存在会降低滤波精度,迭代算法的基本思想是将重力异常估计值代入新的导航解算,以此降低重力异常对滤波估计精度的影响。仿真分析表明所提出的方法能有效提高比力测量的精度,同时表明滤波估计是有偏的,因此还需要采用网格平差等方法来消除系统误差。

Two new methods were presented to improve the accuracy of specific force considering that only post-processing was needed in airborne gravimetry. The first was optimal Kalman filter/smoother, and its estimation was the optimal combination of the estimation of forward/backward Kalman filters. The second was iteration algorithm. Since the gravity anomaly was generally not modeled in the filtering model, the model error would reduce the accuracy of estimation. The basic idea of the iteration algorithm was to reduce the influence of model error using the estimated gravity anomaly in the next navigation calculation. Simulation analysis shows that the methods can improve the accuracy of specific force effectively, but the estimation has deviation, so other methods such as crossover adjustment are needed to remove the systematic error.