Trajectory Estimation Filtering Algorithm for Underwater Equipment Based on UKF - CHEN Ziyue; JI Fuwu; ZHOU Wei (Haiyang Kaifa yu guanli)
The underwater equipment carried by the East China Sea seabed observation network is often lost due to human activities. When solving the trajectory of underwater equipment by inertial navigation, the noise caused by the complex ocean environment and sudden motion leads to large calculation errors. Aiming at the limitation that the classic unscented Kalman filter needs accurate noise model and dynamic model to output better filtering results in the trajectory estimation process, an adaptive filtering algorithm based on Sage-Husa unscented Kalman filter is proposed for solving the trajectory of underwater equipment being towed. Firstly, the implementation process of the classic unscented Kalman filter is explained, and the idea of Sage-Husa adaptive adjustment of noise is introduced; then, on this basis, the prediction residual vector is introduced to reduce the influence of gross error on the filtering result, and a tracking factor is introduced to improve the adaptability of the filter to sudden motion; finally, the effectiveness of the algorithm is verified by simulation experiments and towing experiments. The experimental results show that the adaptive filtering algorithm based on Sage-Husa unscented Kalman filter effectively reduces the divergence error caused by the underwater acoustic environment and sudden motion, and improves the positioning accuracy and positioning stability of underwater equipment when being towed.