To reduce the random error of microelectromechanical system (MEMS) gyroscope, a hybrid method combining improved empirical mode decomposition (EMD) and least squares algorithm (LS) is proposed. Firstly, based on the multiple screening mechanism, intrinsic mode functions (IMFs) from the first decomposition are divided into noise IMFs, strong noise mixed IMFs, weak noise mixed IMFs and signal IMFs. Secondly, according to their characteristics, they are processed again. IMFs from the second decomposition are divided into noise IMFs and signal IMFs. Finally, useful signal is gathered to obtain the final denoising signal. Compared with some other denoising methods proposed in recent years, the experimental results show that the proposed method has obvious advantages in suppressing random error, greatly improving the signal quality and improving the accuracy of inertial navigation.

In recent years, due to the proliferation of inertial measurement units (IMUs) in mobile devices such as smartphones, attitude estimation using inertial and magnetic sensors has been the subject of considerable research. Traditional methods involve probabilistic and iterative state estimation; however, these approaches do not generalize well over continuously changing motion dynamics and environmental conditions. Therefore, this paper proposes a deep learning-based approach for attitude estimation. This approach segments data from sensors into different windows and estimates attitude by separately extracting local features and global features from sensor data using a residual network (ResNet18) and a long short-term memory network (LSTM). To improve the accuracy of attitude estimation, a multi-scale attention mechanism is designed within ResNet18 to capture finer temporal information in the sensor data. The experimental results indicate that the accuracy of attitude estimation using this method surpasses that of other methods proposed in recent years.