We revisit High-Resolution Range Profile (HRRP) classification with aspect-angle conditioning. While prior work often assumes that aspect-angle information is incomplete during training or unavailable at inference, we study a setting where angles are available for all training samples and explicitly provided to the classifier. Using three datasets and a broad range of conditioning strategies and model architectures, we show that both single-profile and sequential classifiers benefit consistently from aspect-angle awareness, with an average accuracy gain of about 7% and improvements of up to 10%, depending on the model and dataset. In practice, aspect angles are not directly measured and must be estimated. We show that a causal Kalman filter can estimate them online with a median error of 5{\textdegree}, and that training and inference with estimated angles preserves most of the gains, supporting the proposed approach in realistic conditions.
Cite
@article{arxiv.2603.00087,
title = {High-Resolution Range Profile Classifiers Require Aspect-Angle Awareness},
author = {Edwyn Brient and Santiago Velasco-Forero and Rami Kassab},
journal= {arXiv preprint arXiv:2603.00087},
year = {2026}
}