Real-time Reduction of Magnetic Noise Associated with Ocean Waves via Sage-Husa Algorithm for Towed Overhauser Marine Geomagnetic Sensor
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摘要: 利用Weaver海浪模型，对拖曳式Overhauser海洋磁场传感器海浪磁噪声与深度、波幅等之间的关系进行了理论分析，证明了在极端海况条件下对海浪磁噪声进行抑制的必要性.为提高海洋磁测灵敏度，提出了一种基于改进的Sage-Husa自适应Kalman算法的海浪磁场噪声抑制方法.仿真结果表明，该方法能在不需要先验的噪声统计或实时参考噪声的情况下，实现磁场噪声协方差的快速收敛；且与常规的Sage-Husa算法相比，改进后的Sage-Husa算法降低了对初始参数的依赖性.另外，设计了一种拖曳式Overhauser海洋磁场传感器测试仪来测试上述算法.对比结果表明该方法不仅实现了磁场噪声统计参数的自适应估计，而且比经典Kalman滤波具有更好的滤波效果；此外，海浪磁噪声的功率谱密度由50 pT/Hz1/2@1Hz下降到6 pT/Hz1/2@1Hz.
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关键词:
- Sage-Husa算法 /
- Kalman滤波器 /
- 拖曳式磁测 /
- Overhauser海洋磁场传感器 /
- 磁场噪声实时抑制 /
- 地球物理
Abstract: Based on Weaver's model, we analyze the magnetic noise variations of the towed Overhauser marine magnetic sensor associated with the different ocean depths, wave periods and amplitudes in theory, proving the necessity of suppressing the magnetic noise in the extreme ocean conditions. In this paper, we propose a novel real-time reduction method of the magnetic noise via an improved Sage-Husa adaptive Kalman filter (KF) to improve the sensor sensitivity. The simulation results show that the proposed method could achieve the fast convergence of the covariance of magnetic noise without the priori noise statistics or real-time reference noise. Furthermore, the dependency on the initial parameters is reduced compared with the standard Sage-Husa algorithm. In addition, we implement a towed Overhauser marine magnetometer to test the proposed method. Over all, the comparison experiments show that the proposed method not only realizes the adaptive estimation of the magnetic noise statistics, but also has better suppression effect than the standard KF. The power spectral density of the magnetic noise is reduced to 6 pT/Hz1/2@1Hz from 50 pT/Hz1/2@1Hz after flitting. -
图 1 Weaver海浪感应磁场模型
Fig. 1. Weaver model for the induced magnetic field from ocean waves
图 2 ω=0.4 π rad/s时的海浪磁场噪声
Fig. 2. Magnetic noise associated with ocean waves (ω=0.4π rad/s)
图 4 当R0分别为0.75 (a)和4.75 (b)时基于Sage-Husa算法的Q和R的估计
Fig. 4. Estimations of Q and R using Sage-Husa algorithm with R0=0.75 (a) and R0=4.75 (b)
图 5 当R0分别为0.75 (a)和4.75 (b)时基于改进后的Sage-Husa自适应算法的Q和R的估计
Fig. 5. Estimations of Q and R using improved Sage-Husa algorithm with R0=0.75 (a) and R0=4.75 (b)
图 7 经典Kalman算法和改进后Sage-Husa自适应算法的抑制效果
Fig. 7. Suppression effect of standard Kalman and improved Sage-Husa algorithm
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