引用本文: 喻敏, 王斌, 王文波, 郑雷. 联合EMD与核主成分分析的激光陀螺信号消噪[J]. 武汉大学学报 ( 信息科学版), 2015, 40(2): 233-237+242.
Yu Min, Wang Bin, Wang Wenbo, Zheng Lei. Laser Gyro Signal De-noising Based on EMD and Kernel Principal Component Analysis[J]. Geomatics and Information Science of Wuhan University, 2015, 40(2): 233-237+242.
 Citation: Yu Min, Wang Bin, Wang Wenbo, Zheng Lei. Laser Gyro Signal De-noising Based on EMD and Kernel Principal Component Analysis[J]. Geomatics and Information Science of Wuhan University, 2015, 40(2): 233-237+242.

## Laser Gyro Signal De-noising Based on EMD and Kernel Principal Component Analysis

• 摘要: 为了有效抑制激光陀螺的随机漂移,提高其惯导精度,提出了一种联合经验模态分解(EMD)和核主成分分析(KPCA)的陀螺信号消噪方法。该方法利用EMD将陀螺信号分解为一组内蕴模态函数(IMF),基于EMD噪声能量分布模型,近似估算各层IMF中的噪声能量;然后利用KPCA分解各层IMF,根据噪声能量自适应地选择应保留的主成分分量,以剔除各层IMF中的噪声实现陀螺信号的消噪。通过实验将该方法与小波去噪算法进行对比,利用交叠式Allan方差和经、纬度误差分析不同方法对陀螺信号的消噪效果。实验结果表明,相比经典的小波消噪方法,本文方法的消噪效果有一定程度的提高,可以更有效地抑制陀螺信号的随机漂移。

Abstract: In order to suppress the random shift error of laser gyro and improve the practical precision of inertial navigation systems，an improved gyro denoising method is proposed that combines empirical mode decomposition(EMD) and kernel principal component analysis(KPC'A).In the proposed algorithm，a gyro signal is decomposed as a series intrinsic mode function（IMFs) by EMD. In turn，the noise energy contained in each IMF is approximately estimated by using the IMF noise energy distribution model，and then，decomposing the each IMF by KPCA，and adaptively selecting the principle components which are should be retained. At last，the denoised gyro signal is obtained by accumulating the each processed IMF by KPCA. A detailed comparison between the proposed method and the wavelet methods is given. The denoising effect of different methods is analyzed by the overlapping Allan variance. Experimental results show that the proposed method performs better in removing noise than classic wavelet methods and can more efficiently suppress the gyro random drift.

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