移动平台全张量重力梯度数据的噪声抑制

Noise reducing of moving platform full tensor gravity gradient data

全张量重力梯度仪器测量数据中包含了大量的白噪声和有色噪声。传统的数字滤波器只能滤除某一频段外的噪声,对于混叠在重力梯度有用信号频段范围内的有色噪声不能很好的对其进行分离。为了同时滤除白噪声和有色噪声,笔者利用卡尔曼滤波器采用增广矩阵法将全张量重力梯度数据中的有色噪声进行估计,在抑制白噪声的同时将有用信号和有色噪声分离,并利用数字滤波器与卡尔曼滤波器的优点,将其结合生了更好的滤波效果,得到了更高质量的梯度信号。通过模型试验验证了本方法对噪声的滤波能力,并满足高精度重力梯度数据处理要求。

The measurements of full tensor gradiometer include a lot of white noise and red noise. The tradi- tional digital filter can only removes the noise completely when it in a specify frequency band, but when the noise and the gradient signal have the same frequency band, the traditional filter cannot separate the noise from the gradi- ent signal well. In order to filter the white noise and red noise simultaneously, the authors use the Kalman filter with augmented matrix convert to estimate the red noise in fu]] tensor gravity gradient data, which achieved the aim that restraining the white noise and separating the red noise with the gradient signal, and combine the advantages of the digital filter and Kalman filter to get higher quality gradient signal. Model data have been used to identify the a- bility of the filters, which have satisfied the high precision requirements of processing the gravity gradient data,