Embedded cubature Kalman filter with adaptive setting of free parameter

The choice of free parameter in embedded cubature Kalman filter (ECKF) is important, and it is difficult to choose an optimal value in practice. To solve this problem, an adaptive method is proposed to determine the value of free parameter of ECKF based on maximum likelihood criterion. By incorporating this method in the third-degree ECKF, a new third-degree adaptive ECKF (AECKF) algorithm is obtained. To further improve the accuracy of the third-degree AECKF, a new fifth-degree AECKF based on the fifth-degree embedded cubature rule is developed. Simulation results show that the proposed algorithms have higher estimation accuracy than existing methods. (C) 2015 Elsevier B.V. All rights reserved.