Multi-scale feature vector reconstruction for aircraft classification using high range resolution radar signatures

Abstract

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.

KEYWORDS:

• Radar imaging
• automatic target recognition
• feature extraction
• multi-scale techniques

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Beihang Zhuobai Program [grant number ZG216S2182]; National Natural Science Foundation of China [grant number 61871010]; Basic Ability Enhancement Program for Young and Middle-aged Teachers of Guangxi [grant number 2019KY0079] and the Project of Guangxi Normal University [grant number 2018BQ013].