Applications of the Kalman Filter in Physical Processes: A Review

This article outlines various specialized adaptations of the Kalman Filter designed to address specific estimation challenges across different domains of Physics. The work demonstrates the significant potential of the Kalman filter to enhance the accuracy and reliability of measurements in Physics, providing robust, real-time, and adaptive estimation capabilities. The paper starts with an extensive introduction to the core of the Kalman filter. A clear description of the different filter categories follows, along with the conditions under which each is applied. Various Kalman filter variants address nonlinear, adaptive, continuous-time, large-scale, and uncertain systems. These include the Extended and Unscented Kalman Filters for nonlinear estimation, Adaptive and Kalman–Bucy filters for changing or continuous dynamics, and Ensemble or Schmidt–Kalman filters for large or reduced-order systems. Robust, Cubature, Probabilistic, and Particle Kalman filters further improve performance under outliers, strong nonlinearities, and non-Gaussian uncertainty. To illustrate the practical relevance, detailed applications in Physics are discussed, including thermodynamics, electromagnetism, high-energy physics, quantum physics, and astrophysics, highlighting how Kalman filtering enhances both predictive accuracy and measurement-informed decision-making.