High Resolution Range Profile Feature Research Articles

High-Resolution Range Profile Feature Transformation Method Based on Amplitude-Phase Modulation Metasurface

Time-modulated metasurfaces have attracted much attention due to their flexible manipulation of electromagnetic waves. Time-varying response leads to spread-spectrum and reallocation of energy. Thus, an amplitude-phase joint modulation (TAJM) method is proposed based on a time-modulated active polarization conversion metasurface (APCM). Taking radar high resolution range profile (HRRP) as the object, the distance transformation and multi-target phenomena are further discovered. The proposed method achieves a flexible manipulation of target amplitude and phase. The relationship between the modulation parameters and HRRP feature is studied. It is revealed that the absorption depth is the dominant factor for the energy distribution of TAJM. The method can be applied to multi-target generation and target feature manipulation.

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

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.

Target Recognition Based on Echo Product Spectrum

High resolution range profile (HRRP) got by discrete Fourier transform has fence effect, which will cause HRRP change with the range shift, and the HRRP features will also appear different even in the same perspective. To solve this problem, the paper proposed the echo product spectrum feature (REPSF), which has no fence effect and eliminates the influnce of random initial phase and range shift. The proposed feature makes full use of the amplitude and phase information of echo signal, and has super-resolution target identification ability. Finally, simulation results verify the correctness of the theory.

HRRP feature extraction based on proportion of divergence criterion

Traditional Linear Discriminant Analysis(LDA) faces the problem of tending to keep the separability of the class pairs having large within-class distances,while discarding the separability of those having small within-class distances.Based on the viewpoint that the feature subspace should uniformly keep the separability of each class pair,a new criterion,i.e.,the Proportion of Divergence(PD),was presented.PD criterion was the mean of the proportion of the subspace divergence to original space divergence of each class pair.The solution of the Linear Discriminant Analysis(LDA) maximizing PD criterion(PD-LDA) was also presented.PD-LDA was used to perform feature extraction in the amplitude spectrum space of High Resolution Range Profile(HRRP).Shortest Euclidian distance classifier and Support Vector Machine(SVM) classifier were designed to evaluate the recognition performance.The experimental results for measured data show that,compared with traditional LDA,PD-LDA reduces data dimension remarkably and improves recognition rate effectively.

A new feature extraction method using the amplitude fluctuation property of target HRRP for radar automatic target recognition

Due to the aspect sensitivity of high-resolution range profile (HRRP), traditional radar HRRP target recognition methods usually use average profile within some target-aspect region as the target-aspect template. Actually, the amplitude fluctuation property of target HRRP also represents some feature information of the target. Based on the scattering center model, a new feature extraction method using the amplitude fluctuation property of target HRRP is proposed in this paper. The weighted HRRP feature extracted by the new method can represent the scatterer distribution in every range cell, thereby it can describe the scattering property of the target better. The experimental results based on measured data show that the new feature extraction method can greatly improve recognition performances.