An Adaptive SSUKF Based on Akaike Information Criterion to Optimize the Distribution Entropy of the Innovation

Abstract

At present, integrating the Global Navigation Satellite System (GNSS), microelectromechanical system (MEMS), and odometer (OD) is the most practical and low-cost vehicle multifusion navigation system. However, time-varying noise due to satellite signal rejection can lead to serious degradation or even inaccurate positioning accuracy of the system. To overcome this problem, an adaptive spherical simplex unscented Kalman filter (SSUKF), which optimizes the distribution entropy of the innovation based on the Akaike information criterion, is proposed. Initially, the algorithm optimizes the distribution entropy of the SSUKF innovation sequences by considering the Akaike information criterion. Subsequently, it constructs a dynamic equation of the sliding window using the residual and innovative sequences based on covariance matching. Furthermore, the algorithm estimates and adjusts the statistical characteristics of the systematic process and measurement noise online and improves the adaptive ability of the SSUKF. The algorithm overcomes the problem of degradation and dispersion of the filtration accuracy of the SSUKF when there are unknown, inaccurate, or uncertain noise statistics. Finally, simulation and integrated navigation of actual tests were performed. The test outcomes indicate that the proposed algorithm reduces the errors of the east and north velocities by 67.76% and 70.29%, respectively, with root mean square error (RMSE) values of 0.1449 and 0.1308 m/s, respectively. Additionally, when compared to the SSUKF, the proposed algorithm reduces the errors of the latitude and longitude by 56.55% and 81.78%, respectively, with RMSE values of 3.1072 and 1.6076 m, respectively.