An ANN-Based Data Fusion Algorithm for INS/CNS Integrated Navigation System
Di Liu, Xiyuan Chen
Abstract
For INS/CNS integrated navigation system, the performance of data fusion algorithms based on the Kalman filter is seriously degraded when the subsystems have discrepant sampling-frequency. Therefore, a fusion algorithm based on locally weighted linear regression (LWLR), long short-term memory (LSTM), and cubature Kalman filter (CKF) is proposed. First, we construct a deeper coupled INS/CNS system model based on the low-frequency error of the star sensor. Then, during the CNS data sampling interval, LWLR is employed to fit the CNS data, and CKF is used to fuse the fitted CNS data with INS data. And the estimated error calculated using CKF is optimized by LSTM. Finally, the experimental results show that the proposed algorithm suppresses the divergence problem caused by different sampling-frequency and improves the attitude estimation accuracy of the navigation system.