自适应卡尔曼滤波在航空重力异常解算的应用研究

Application research on adaptive Kalman filtering for airborne gravity anomaly determination

依据航空重力测量基本原理,构建了航空重力异常解算的卡尔曼滤波模型,将新息自适应卡尔曼滤波器(IAE,Innovation based Adaptive Estimation)应用于量测噪声未知的航空重力异常解算.针对IAE滤波器滑动窗口宽度难以准确确定的问题,通过对多个不同滑动窗口新息协方差估计的加权平均,获得改进的IAE滤波器,该IAE滤波器不仅具有量测噪声自适应估计能力,还能实现滑动采样窗口的优化选取.试验结果表明,IAE滤波器可以降低因量测噪声统计信息不明引起的解算误差,改进IAE解算的重力异常误差约为1mGal.

Based on the basic principle of airborne gravimetry,Kalman filtering model for airborne gravity anomaly determination is established and innovation-based adaptive estimation Kalman filter（IAE）is applied to estimate the airborne gravity anomaly when measurement covariance is unknown.At the same time,improved IAE filter installing several innovation covariance estimators is designed to reduce the difficulty of sampling window width selection.It is can be seen from the experimental result that IAE is able to reduce gravity anomaly determination errors caused by lack of statistical information of measurement noise.Moreover,the improved IAE filter can decrease the rate of determination errors introduced by the improper selection of sampling window width,and the error of gravity anomaly determined by improved IAE is nearly 1mGal.