ABSTRACT
The ocean-going environment is complex and changeable with great uncertainty, which poses a huge challenge to the navigation ability of ships working in the pelagic ocean. In this paper, in an attempt to deal with the complex uncertain interference that the environment may bring to the strap-down inertial navigation system/polarization navigation system/geomagnetic navigation system (SINS/PNS/GMNS) integrated navigation system, the multi-mode switching variational Bayesian adaptive Kalman filter (MMS-VBAKF) algorithm is proposed. To be more specific, to identify the degrees of measurement interference more effectively, we design an interference evaluation and multi-mode switching mechanism using the original polarization information and geomagnetic information. Through this mechanism, the interference to the SINS/PNS/GMNS navigation system is divided into three cases. In case of slight interference (case SI), the variational Bayesian method is adopted directly to solve the problem that the statistical characteristics of measurement noise are unknown. By the fixed-point iteration mechanism, the statistical properties of the measurement noise and the system states can be estimated adaptively in real time. In case of interference-tolerance (case TI), the estimation of the statistical characteristics of measurement noise need to weigh the measurement information at the moment and a priori value information comprehensively. In case of excessive interference (case EI), the SINS/PNS/GMNS integrated navigation system will perform mode switching and filtering system reconstruction in advance. Then, the information fusion and navigation states estimation can be completed. Consequently, the reliability, robustness, and accuracy of the SINS/PNS/GMNS integrated navigation system can be guaranteed. Finally, the effectiveness of the algorithm is illustrated by the simulation experiments.
ABSTRACT
Polarization (POL) navigation is inspired by insects' behavior of precepting celestial polarization patterns to orient themselves. It has the advantages of being autonomous and having no accumulative error, which allows it to be used to correct the errors of the inertial navigation system (INS). The integrated navigation system of the POL-based solar vector with INS is capable of 3D attitude determination. However, the commonly used POL-based integrated navigation system generally implements the attitude update procedure without considering the performance difference with different magnitudes of the angles between the solar-vector and body-axes of the platform (S-B angles). When one of the S-B angles is small enough, the estimated accuracy of the attitude angle by the INS/POL is worse than that of the strapdown inertial navigation system. To minimize the negative impact of POL in this situation, an attitude angular adaptive partial feedback method is proposed. The S-B angles are used to construct a partial feedback factor matrix to adaptively adjust the degree of error correction for INS. The results of simulation and real-world experiments demonstrate that the proposed method can improve the accuracy of 3D attitude estimation compared with the conventional all-feedback method for small S-B angles especially for yaw angle estimation.