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| Citation: | WANG Jingjing, LIU Zheng, XIE Rong, et al. HRRP target recognition method for full polarimetric radars by combining Cameron decomposition and fusing RKELM[J]. Journal of Radars, 2021, 10(6): 944–955. doi: 10.12000/JR21099
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HRRP Target Recognition Method for Full Polarimetric Radars by Combining Cameron Decomposition and Fusing RKELM
DOI: 10.12000/JR21099
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Abstract
A recognition method combining Cameron decomposition and fusing Reduced Kernel Extreme Learning Machine (RKELM) is proposed for the Full Polarimetric (FP) High Resolution Range Profile (HRRP)-based radar target recognition task. In the feature extraction phase, Cameron decomposition is exploited to define the projection component of the target on the standard scatterers. Through analysis, the projection components on three scattering bases, i.e., trihedral, dihedral, and 1/4 wave device, are selected as target features, which achieve more detailed descriptions of the target characteristics. In the classification phase, considering the instability of the recognition performance of the RKELM algorithm, the RKELM based on prototype clustering preprocessing is first proposed. Then, to improve the recognition performance, we proposed the feature level fusing RKELM and the decision level fusing RKELM to fuse the three projection components of the targets. The experiments compared the performance of the proposed recognition method and the state-of-the-art methods using the FP HRRP data from 10 civilian vehicles. The results demonstrate that the projection features by Cameron decomposition exhibit higher separability and better noise robustness, and that the feature level fusing RKELM has better generalization performance with a large number of training samples, but the decision level fusing RKELM was better with a small number of training samples. -
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References
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Figure 1. Unit disc representation of
$z$ - Figure 2. Feature level fusing RKELM network based on prototype clustering preprocessing
- Figure 3. Decision level fusing RKELM network based on prototype clustering preprocessing
- Figure 4. Diagram of simulated echo from civilian vehicles
- Figure 5. RGB images of the Cameron projection features from 10 civilian vehicles
- Figure 6. Average recognition rates versus size of training data
- Figure 7. Average recognition rates versus SNR