High-Resolution Range Profile (HRRP) is effective in various Radar Automatic Target Recognition problems. Multi-scale techniques have been verified in aircraft target HRRP feature enrichment to achieve aircraft recognition performance optimization. The involvement of multiple training-classification procedures in existing multi-scale methods results in tremendous resource consumption which challenges their real-time classification performance and application significance. This paper introduces a novel method that enables multi-scale HRRP features to be manipulated under a single scale for efficiency enhancement. Numerical analysis indicates that multi-scale intensity variations for particular HRRP scatterers could be modeled as Gaussian noises. Linear Discriminant Analysis and Singular Value Decomposition techniques are applied on reconstructed feature vectors for better separability and noise tolerance capability. Experimental results from dynamic aircraft recognition experiments verify the expected efficiency enhancement of the proposed method while maintaining comparable classification accuracy and better noise tolerance performance.

高分辨率距离剖面 (HRRP) 在各种雷达自动目标识别问题中都很有效。多尺度技术已在飞机目标 HRRP 特征丰富中得到验证，以实现飞机识别性能优化。现有多尺度方法中涉及多个训练分类程序导致巨大的资源消耗，这对其实时分类性能和应用意义提出了挑战。本文介绍了一种新方法，该方法使多尺度 HRRP 特征能够在单一尺度下进行操作以提高效率。数值分析表明，特定 HRRP 散射体的多尺度强度变化可以建模为高斯噪声。线性判别分析和奇异值分解技术应用于重建的特征向量，以获得更好的可分离性和噪声容限能力。来自动态飞机识别实验的实验结果验证了所提出方法的预期效率提升，同时保持了可比的分类精度和更好的噪声容限性能。