Problem
Research questions and friction points this paper is trying to address.
High-Resolution Range Profile
aspect-angle awareness
classification
High-Resolution Range Profile Classifiers Require Aspect-Angle Awareness
🤖 AI Summary
研究通过在训练和推理中利用显式的视角角度信息，提高了高分辨率距离像分类器的性能，并使用因果卡尔曼滤波器在线估计角度以适应实际条件。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
aspect-angle awareness
High-Resolution Range Profile (HRRP)
causal Kalman filter
classification accuracy
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