Improved exponential weighted moving average based measurement noise estimation for strapdown inertial navigation system/doppler velocity log integrated system

Abstract

The strapdown inertial navigation system (SINS) with integrated Doppler velocity log (DVL) is widely utilised in underwater navigation. In the complex underwater environment, however, the DVL information may be corrupted, and as a result the accuracy of the Kalman filter in the SINS/DVL integrated system degrades. To solve this, an adaptive Kalman filter (AKF) with measurement noise estimator to provide noise statistical characteristics is generally applied. However, existing methods like moving windows (MW) and exponential weighted moving average (EWMA) cannot adapt to a dynamic environment, which results in unsatisfactory noise estimation performance. Moreover, the forgetting factor has to be determined empirically. Therefore, this paper proposes an improved EWMA (IEWMA) method with adaptive forgetting factor for measurement noise estimation. First, the model for a SINS/DVL integrated system is established, then the MW and EWMA based measurement noise estimators are illustrated. Subsequently, the proposed IEWMA method which is adaptive to the various environments without experience is introduced. Finally, simulation and vehicle tests are conducted to evaluate the effectiveness of the proposed method. Results show that the proposed method outperforms the MW and EWMA methods in terms of measurement noise estimation and navigation accuracy.

Neural Network-Based Dynamic Segmentation and Weighted Integrated Matching of Cross-Media Piano Performance Audio Recognition and Retrieval Algorithm

“…Different frames of gesture sequence images can be used as other input channels, and then 2D convolutional neural networks are applied. However, 2D convolutional neural networks lack the utilization of interframe motion information [26]. In contrast, 3D convolutional neural network methods can compensate for this drawback by entirely using the spatiotemporal feature information between image sequences.…”

Sea current relative navigation using interacting multiple model filter with adaptive fading technique

“…Among them, systems such as LBL (Zhang et al, 2018) and USBL (Wang et al, 2020) require a separate infrastructure. On the other hand, DVL (Yao et al, 2017(Yao et al, , 2019Zhu and He, 2020;Hou et al, 2021) and EM-log (Vaisgant et al, 2011;Sun et al, 2015) are equipped in the vehicle's body and are suitable for voyages over long distances. These two sensors are similar in a sense that both provide the vehicle's relative velocity to the sea current to perform the relative navigation, but their principles are quite different.…”