Multidimensional Cubature Kalman Filter (CKF) Implementation

When conducting simulations in MATLAB, you can integrate this multidimensional Cubature Kalman Filter (CKF) program into your simulation examples. The implementation utilizes the third-degree spherical-radial cubature rule to approximate multidimensional integrals encountered in nonlinear Bayesian filtering, providing superior accuracy for high-dimensional systems compared to traditional filters. This program assists in enhanced data processing, yielding more accurate and reliable simulation results through its numerically stable implementation of time-update and measurement-update phases. You can directly invoke the function without manual coding or complex operations, as it handles state prediction, covariance propagation, and measurement correction automatically using cubature points transformation. The algorithm employs 2n cubature points (where n is the state dimension) to capture posterior statistics efficiently. We recommend incorporating this function into your projects to streamline nonlinear filtering tasks and achieve improved estimation performance with reduced development time.