High-Resolution Range Profile Classifiers Require Aspect-Angle Awareness

TL;DR

This paper demonstrates that incorporating aspect-angle information into HRRP classifiers significantly improves accuracy, and that angles can be effectively estimated online with Kalman filters to retain these benefits in real-world scenarios.

Contribution

The study shows that explicit aspect-angle conditioning enhances HRRP classification performance and that online estimation of angles with Kalman filters maintains these improvements.

Findings

Aspect-angle awareness improves classification accuracy by up to 10%.

Kalman filter estimates angles with median error of 5 degrees.

Training with estimated angles retains most of the accuracy gains.

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.