Heading Angle

Heading angle estimation, crucial for navigation and orientation in various applications, focuses on accurately determining the direction of travel. Current research emphasizes robust methods for estimating heading angle even under challenging conditions like low speeds, noisy sensor data, or magnetic interference, employing techniques such as Kalman filtering, deep learning architectures (e.g., neural networks designed for regression), and novel signal processing approaches based on transformations like the Radon transform. These advancements improve the accuracy and reliability of heading angle estimation across diverse domains, including autonomous vehicles, robotics, and underwater navigation, leading to safer and more efficient systems.