Kalman Filtering and Estimation Techniques in the Synthesis of Intelligent Adaptive Systems

Abstract

The development of intelligent adaptive systems requires robust estimation and filtering methods to ensure system stability, reliability, and performance in dynamic environments. Kalman filtering, as one of the most powerful and widely-used estimation techniques, plays a central role in sensor fusion, state estimation, and control adaptation. This paper explores the integration of Kalman filters within the synthesis process of intelligent adaptive systems, highlighting both linear and extended Kalman filter (EKF) variants. We provide comparative analysis with alternative estimation techniques and discuss practical implementations in real-time adaptive control, robotics, and autonomous navigation. Simulation results demonstrate the effectiveness of Kalman filtering in improving estimation accuracy and control efficiency under noisy and uncertain conditions.