Azimuth-Only NED Angle Estimation in Electronic Warfare: Optimization and Deep Segmentation Approaches

This article addresses the problem of estimating both azimuth and elevation angles in the North-East-Down frame using only azimuth measurements obtained in the body frame of an airborne electronic warfare system. We propose two different approaches that exploit the maneuverability of the measurement platform to recover 3-D angle of arrival information from limited input data. The first approach employs a nonconvex optimization framework that directly utilizes time-varying azimuth measurements and platform orientation. The second approach reformulates the estimation problem as an image segmentation task, where azimuth measurements are transformed into 2-D representations in which target angles correspond to curve intersections. Convolutional and transformer-based deep segmentation models are then used to infer the targets spatial angles. Experimental results demonstrate that deep segmentation significantly outperforms the optimization-based method in terms of estimation accuracy and robustness under varying noise and maneuver conditions.