An Improved MIMU Joint Noise Reduction Method

In practical applications of MIMU, due to the influence of measurement noise and external environment, the raw data contains many outliers and noise signals, which reduces its system performance. In view of the above problems, this paper combines the traditional AKF algorithm with the LSTM network to obtain a joint noise reduction algorithm. Based on the completion of noise modeling and model parameter update, the construction principle of the joint noise reduction algorithm is briefly analyzed. This method makes the advantages of AKF and LSTM complement each other, and overcomes the shortcomings of traditional AKF's poor stability. The experimental results show that the method can effectively suppress the measurement noise interference and improve the accuracy of pure inertial navigation.