Adaptive Mobile Target Tracking for Smart Antenna Systems

Adaptive mobile target tracking plays a crucial role in smart antenna systems. To achieve this objective, we have developed source code implementing the Kalman filter algorithm. The Kalman filter is an optimal recursive algorithm that estimates system states by combining prior knowledge with real-time measurement data. This implementation typically involves several key functions: state prediction using system dynamics models, measurement update procedures, and covariance matrix calculations for error estimation. The algorithm operates through two main phases - prediction and correction - where it first projects the current state forward using the system model, then refines this prediction using actual measurements. In smart antenna applications, this filter can effectively track dynamic parameters such as target position, velocity, and acceleration. The code implementation likely includes matrix operations for state transition and measurement models, recursive covariance updates, and gain calculations. This enhances the antenna's adaptive capabilities by providing real-time target parameter estimation, significantly improving tracking accuracy and system responsiveness in dynamic environments. Consequently, the application of Kalman filtering in smart antenna systems holds substantial practical importance for modern communication and radar applications.