Cubature Kalman Filter Implementation for Signal Filtering

I have developed a filtering program using the Cubature Kalman Filter (CKF) that effectively reduces signal noise while enhancing data accuracy and reliability. The implementation employs third-degree spherical-radial cubature rules for numerical integration, providing superior nonlinear estimation compared to traditional filters. During development, I adhered to software engineering best practices including modular programming with separate functions for time update, measurement update, and covariance propagation, along with comprehensive code documentation to ensure maintainability and scalability. Prior to testing, I conducted thorough data analysis and preprocessing steps including outlier detection and signal normalization to ensure robust performance across various scenarios and data types. The algorithm efficiently handles nonlinear systems through deterministic sampling points that approximate Gaussian-weighted integrals without linearization errors. Overall, this filtering solution represents a high-quality, computationally efficient tool that can significantly enhance your data analysis and processing workflows.