基于奇异值分解的高精度重力仪信号处理方法

在分析基于矩阵奇异值分解理论的滤波算法基础上,将其应用到高精度海洋重力仪系统信号处理中.在信号处理过程中,首先采用延迟法理论重构系统的相空间,得到吸引子轨迹矩阵,然后对轨迹矩阵进行奇异值分解,用部分奇异值重构有用信号的最佳逼近矩阵,并与自适应卡尔曼滤波进行了对比分析,以实际信号与处理后信号的信噪比作为衡量2种信号处理方法好坏的依据.理论分析和仿真实验表明,奇异值分解滤波方法和自适应卡尔曼滤波都能在一定程度上消除干扰噪声对重力异常信号的影响,但在相同背景条件下,奇异值分解滤波的性能优于自适应卡尔曼滤波.

Signal processing method of precise gravimeter based on singular value decomposition

Compared with adaptive Kalman filtering, the theory of filtering based on matrix singular value decomposition (SVD) is analyzed and applied to process the signal measured by precise gravimeter. Preliminary work is to form a trajectory matrix with time delay embedding theory during the signal processing. SVD is then used to distinguish the signal from the noise. The signal to noise ratio (SNR) is used as the index for evaluating the performance of the signal processing methods. Theoretical analysis and emulation experiments indicate that both SVD filtering and adaptive Kalman filtering are effective in alleviating the effects of different noise, but the performance of SVD filtering is better than that of adaptive Kalman filtering.