[2603.00087] High-Resolution Range Profile Classifiers Require Aspect-Angle Awareness

Electrical Engineering and Systems Science > Signal Processing arXiv:2603.00087 (eess) [Submitted on 16 Feb 2026] Title:High-Resolution Range Profile Classifiers Require Aspect-Angle Awareness Authors:Edwyn Brient, Santiago Velasco-Forero (CMM), Rami Kassab View a PDF of the paper titled High-Resolution Range Profile Classifiers Require Aspect-Angle Awareness, by Edwyn Brient and 2 other authors View PDF Abstract: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. Subjects: Signal Processing (eess.SP); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2603.00087 [...