Velocity Estimation for Vehicle-Mounted SAR Based on Deep-Learning Framework

With the development of intelligent driving technology, vehicle-mounted synthetic aperture radar (SAR) imaging, which is regarded as one of the most fundamental assists, has become an important and focal topic. As for automotive SAR imaging, the primary task is to estimate the velocity of the moving platform. Due to the volatile and complex road conditions, it does expense tremendous efforts for accurate velocities acquired with additional motion sensors. In this article, based on exploiting the geometrical relations between the moving platform and near-ground echoes, a velocity estimation scheme is deduced with a single radar sensor only. Then, with the proposed geometric model, an accurate velocity can be obtained and quantitatively analyzed. In addition, to further improve the efficiency of velocity estimation, we establish a real-world SAR echo dataset and transform the conventional equipment-dependable problem into a data-driven method based on a deep neural network. The efficiency and effectiveness of the proposed methods are verified by SAR data collected from real-world scenes.