Kalman Filter: Research Paper and Implementation with Comprehensive Annotations

I recently published a research paper on Kalman Filter along with its implementation code on my technical blog. The paper underwent rigorous peer review by domain experts, with all conclusions thoroughly validated. The publication provides comprehensive explanations of Kalman Filter concepts and underlying principles, while the accompanying code includes line-by-line annotations to facilitate understanding and practical implementation. The filtering algorithm demonstrates exceptional precision through extensive testing, incorporating key functions for state prediction (using prior estimates and system dynamics) and measurement updates (applying Bayesian correction with covariance matrices). The implementation handles noise covariance matrices optimally and includes stability checks for numerical robustness. I believe this resource will significantly benefit researchers and engineers working with Kalman Filters, serving as a valuable reference for both academic studies and real-world engineering projects requiring optimal state estimation.