A novel CKF method for target tracking

This paper presents a new target tracking method. The presented method which named marginalized cubature Kalman filter is based on standard cubature Kalman filter and marginalized moment estimator. The marginalized moment estimator uses sigma-points sampling and Guass-Hermite integration to estimate the mean and covariance. The proposed algorithm which is called MCKF in short, uses marginalized moment estimator to calculate the state´s mean and covariance in the CKF framework and gets a better accuracy and keep the covariance matrix being positive definite. Simulation indicates the presented algorithm´s feasibility and improved performance.