Multidimensional Cubature Kalman Filter

The source program implements a high-dimensional cubature Kalman filter designed for dynamic system state estimation. This algorithm provides a robust solution for state estimation across various domains including signal processing, control engineering, and navigation systems. As a recursive Bayesian filter, it processes sequential measurement data to continuously update system state estimates. The implementation employs cubature rules to numerically approximate Gaussian-weighted integrals, enabling efficient handling of high-dimensional state spaces. Key algorithmic components include: - Cubature point generation using spherical-radial transformations - Time update phase propagating state estimates through system dynamics - Measurement update phase incorporating new observations - Statistical linearization techniques for nonlinear systems The high-dimensional capability allows managing systems with numerous state variables, while the cubature approach ensures computational efficiency with large measurement datasets. The source code provides optimized matrix operations and numerical stability safeguards, making it suitable for complex real-time applications requiring accurate state estimation under nonlinear conditions and substantial dimensionality.