理论模型分析卡尔曼滤波在航空全张量磁力梯度测量中的应用效果

航空全张量磁力梯度测量数据中包含非常复杂的运动噪声,在频谱图中从低频至高频均有分布,并以白噪声为主,如何有效压制运动噪声是一个较大的挑战。传统的数字滤波只能滤除指定频段的噪声,对于混叠在全张量磁力梯度有用信号中的噪声不能有效分离。鉴于卡尔曼滤波是一种快速、高效和实时的最优估计方法,笔者将其应用到航空全张量磁力梯度数据处理中,搭建合理的状态方程和观测方程,通过模型实验验证了方法的有效性,结果显示全区噪声衰减因子优于0.92,即能够去除92%以上噪声成分,全区均方误差优于10 pT/m,可应用于航空全张量磁力梯度数据实时处理。

Application effect of Kalman filter in airborne full tensor magnetic gradient measurement based on theoretical model

The aero full tensor magnetic gradient data contain complex motion noise,which is distributed from low frequency to high frequency in the spectrum,and is mainly white noise.So,how to effectively suppress the motion noise is a great challenge.The traditional digital filtering can only filter the noise with the specified frequency band,but it can not effectively separate the noise mixed in the full tensor magnetic gradient useful signal.In view of the fact that Kalman filter is a fast,efficient and real-time optimization estimation method,the authors applied it to the aero magnetic full tensor gradient data processing,and built the state equation and observation equation reasonably.Model test proves that the method is effective and can be applied to real-time processing of aeromagnetic full tensor gradient data.