High-Resolution Range Profile Classifiers Require Aspect-Angle Awareness

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.

Signal Processing (eess.SP); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2603.00087 [eess.SP]

(or arXiv:2603.00087v2 [eess.SP] for this version)

https://doi.org/10.48550/arXiv.2603.00087 Focus to learn more arXiv-issued DOI via DataCite

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