High-Order Cubature Kalman Filter

In this article, we discuss the High-Order Cubature Kalman Filter (CKF) program and its applications in filtering and tracking. CKF is a filtering algorithm based on the Extended Kalman Filter (EKF) framework that enables improved state estimation, particularly for high-order systems. The key advantage of CKF lies in its ability to enhance estimation accuracy by increasing the number of cubature points in high-dimensional spaces. The implementation typically involves calculating spherical-radial cubature points through numerical integration methods like the third-degree spherical-radial rule. For multi-target tracking scenarios, CKF can maintain multiple parallel filter instances with separate state vectors and covariance matrices. The algorithm's core functions include state prediction using system dynamics models and measurement update through Bayesian inference. Additionally, CKF finds extensive application in multi-target tracking domains, making it a valuable tool for handling complex tracking scenarios. In summary, CKF represents a highly useful algorithm and serves as an important tool in the fields of filtering and tracking, with implementations often involving matrix operations for covariance propagation and resampling techniques for maintaining estimation quality.