Extended Kalman Filter Localization Algorithm for TDOA/AOA Positioning

This article explores the Extended Kalman Filter (EKF) localization algorithm for TDOA/AOA positioning. This algorithm utilizes sensor measurements of Time Difference of Arrival (TDOA) and Angle of Arrival (AOA) to determine user positions with high precision in complex environments such as urban areas and indoor settings. The EKF implementation typically involves linearizing nonlinear measurement models using Jacobian matrices to handle the nonlinear relationships between sensor measurements and position states. Key algorithmic components include state prediction using motion models and measurement update phases that fuse TDOA/AOA observations. We examine the algorithm's strengths in handling measurement noise and dynamic system changes, while also addressing limitations in computational complexity and linearization errors. Performance comparisons with other localization algorithms like particle filters and unscented Kalman filters are discussed, highlighting trade-offs in accuracy, computational load, and robustness. The article concludes with future development directions including multi-sensor fusion implementations and potential applications in emerging technologies such as Internet of Things (IoT) networks and autonomous driving systems, where improved localization accuracy is critical for navigation and safety functions.