ABSTRACT
Multisensor information fusion has been widely used in the military, defense, and high-tech fields. In multisensor information fusion theory, redundant and complementary information of multiple sensor measurements is used to improve system accuracy. In practice, asynchronous information fusion problem are often encountered. This chapter presents a nonlinear fusion algorithm for asynchronous multisensor data. Cubature Kalman filter (CKF) is proposed to solve the nonlinear filtering problem based on the spherical-radial cubature criterion. The CKF used a third-degree cubature rule to numerically compute the mean and variance of the probability distribution with cubature points, so the estimation accuracy can achieve third order or higher. The chapter utilizes the novel algorithm based on the CKF for its numerical stability and compares it with the nonlinear fusion algorithm of asynchronous sensors based on unscented Kalman filter (UKF). The simulation results show that the accuracy of the proposed new algorithm based on CKF is higher than that of the existing algorithm based on UKF.
