Recursive Gain Kalman Filter Package for Maneuvering Target Tracking

This document presents a recursive gain Kalman filter package I developed specifically for maneuvering target detection and tracking applications. The package implements advanced filtering algorithms that incorporate both target mathematical models and noise models to handle target maneuverability effectively. Through comprehensive simulations, the system calculates average observation errors to validate performance. The codebase features strategically placed annotations at critical algorithm sections, including state prediction updates (using kinematic equations), measurement covariance handling, and recursive gain calculation routines. Key implemented functions include dynamic model adaptation for maneuvering targets, noise covariance matrix optimization, and real-time tracking error computation. Additional advantages of this package include robust performance under various maneuvering scenarios and an intuitive interface that simplifies parameter configuration and result visualization. This resource aims to support researchers in radar and target tracking domains by providing both theoretical foundations and practical implementation insights.