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HRRP Target Recognition Method for Full Polarimetric Radars by Combining Cameron Decomposition and Fusing RKELM
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摘要: 该文针对传统全极化高分辨一维距离像(HRRP)雷达目标识别问题，提出了结合Cameron分解和融合简化核极限学习机(RKELM)的目标识别方法，旨在提高全极化HRRP目标识别性能。在特征提取阶段，所提方法利用Cameron分解定义了目标在各个标准散射体上的投影分量。通过分析，将目标在三面角、二面角和1/4波长器件这3个散射基上沿距离维的投影分量作为目标特征，实现对目标散射特性更加精细化的描述。在分类阶段，考虑到RKELM算法识别性能的不稳定性，提出了一种基于原型聚类预处理的RKELM方法，并在此基础上设计了特征级融合RKELM网络和决策级融合RKELM网络，以对投影特征进行融合分类。实验部分利用10类民用车辆的全极化HRRP数据将所提识别方法和现有方法进行了对比，结果表明：(1)所采用的Cameron分解投影特征表现出了较高的可分性和噪声稳健性；(2)当训练样本数较多时，特征级融合RKELM算法的泛化性能较好；当训练样本数较少时，决策级融合RKELM的泛化性能较好。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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图 1 对称散射体所对应的
$z$ 值的单位圆盘表示Figure 1. Unit disc representation of
$z$ values and the corresponding symmetric scatterers
图 2 基于原型聚类预处理的特征级融合RKELM网络结构
Figure 2. Feature level fusing RKELM network based on prototype clustering preprocessing
图 3 基于原型聚类预处理的决策级融合RKELM网络结构
Figure 3. Decision level fusing RKELM network based on prototype clustering preprocessing
图 5 10类民用车辆的Cameron投影特征RGB图
Figure 5. RGB images of the Cameron projection features from 10 civilian vehicles
表 1 z值与对应的对称散射体
Table 1. z values and the corresponding symmetric scatterers
对称散射体类型 z 对称散射体类型 z 三面角 1.0 圆柱体 0.5 二面角 –1.0 窄二面角 –0.5 偶极子 0 1/4波长器件 $\pm \rm i$ 
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表 2 任意散射体与标准对称散射体之间的相似性计算方式
Table 2. Calculation of similarity between arbitrary scatterers and standard symmetric scatterers
散射体 ${z_0}$ $\varphi \left( {z,{z_0}} \right)$ 散射体 ${z_0}$ $\varphi \left( {z,{z_0}} \right)$ 三面角 1.0 $\sqrt {\dfrac{1}{2} + \dfrac{{{z_{\rm{r}}}}}{{1 + {{\left| z \right|}^2}}}} $ 圆柱体 0.5 $\sqrt {\dfrac{1}{5} + \dfrac{{3 + 4{z_{\rm{r}}}}}{{5\left( {1 + {{\left| z \right|}^2}} \right)}}} $ 二面角 –1.0 $\sqrt {\dfrac{1}{2} - \dfrac{{{z_{\rm{r}}}}}{{1 + {{\left| z \right|}^2}}}} $ 窄二面角 –0.5 $\sqrt {\dfrac{1}{5} + \dfrac{{3 - 4{z_{\rm{r}}}}}{{5\left( {1 + {{\left| z \right|}^2}} \right)}}} $ 偶极子 0 $\sqrt {\dfrac{1}{{1 + {{\left| z \right|}^2}}}} $ 1/4波长器件 $\pm \rm i$ $\sqrt {\dfrac{1}{2} + \dfrac{{\left| {{z_{\rm{i}}}} \right|}}{{\left( {1 + {{\left| z \right|}^2}} \right)}}} $
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表 3 标准散射体的Cameron分解参数和相似性参数
Table 3. Cameron decomposition parameters of the standard scatterers and their similairity parameters
散射体类型 最小对称分量能量 最大对称分量能量 与标准散射体之间的相似性 三面角 二面角 偶极子 圆柱体 窄二面角 1/4波长器件 三面角 0 1.0 1.000 0 0.707 0.949 0.316 0.707 二面角 0 1.0 0 1.000 0.707 0.316 0.949 0.707 偶极子 0 1.0 0.707 0.707 1.000 0.894 0.894 0.707 圆柱体 0 1.0 0.949 0.316 0.894 1.000 0.600 0.707 窄二面角 0 1.0 0.316 0.949 0.894 0.600 1.000 0.707 1/4波长器件 0 1.0 0.707 0.707 0.707 0.707 0.707 1.000 左螺旋 0.5 0.5 0 1.000 0.707 0.316 0.949 0.707 右螺旋 0.5 0.5 0 1.000 0.707 0.316 0.949 0.707
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表 4 特征改进的ReliefF可分性值
Table 4. Seperability measure of features by the modified ReliefF
特征 可分性 Cameron分解 2.766 全极化幅度 2.594 Cloude分解 1.780 Krogager分解 2.600 HH 2.084 VV 2.455 HV 1.978
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表 5 所提方法和对比方法在不同隐层节点数下的识别性能(%)
Table 5. Recognition performance of the proposed methods and comparative methods with different number of hidden layer nodes (%)
隐层节点数 Cameron+特征级RKELM 幅度+特征级RKELM Cameron+特征级RKELM(随机) Cloude+特征级RKELM Krogager+特征级RKELM Cameron+决策级RKELM 幅度+决策级RKELM 50 77.3 73.4 75.7 66.7 73.5 66.7 64.5 100 87.3 84.6 86.3 76.3 84.4 79.9 77.7 150 91.4 89.8 90.1 81.0 89.3 85.6 84.2 200 93.6 92.6 93.1 83.9 92.0 88.9 88.0 250 94.9 94.2 94.5 85.9 93.5 91.1 90.2 300 95.7 95.1 95.4 87.3 94.5 92.6 91.7 350 96.3 95.7 96.1 88.5 95.2 93.6 92.9 400 96.7 96.1 96.5 89.3 95.7 94.3 93.7 450 97.0 96.5 96.8 90.0 96.1 94.9 94.3 500 97.2 96.7 97.1 90.6 96.3 95.3 94.8 隐层节点数 Cameron+决策级RKELM(随机) Cloude+决策级RKELM Krogager+决策级RKELM HH+聚类-RKELM VV+聚类-RKELM HV+聚类-RKELM 50 66.0 59.2 65.3 51.3 52.2 43.0 100 79.0 68.8 77.9 62.0 65.1 52.2 150 84.7 74.3 84.1 69.2 72.7 56.3 200 88.4 77.8 87.0 74.4 77.8 58.8 250 90.7 80.4 89.6 78.1 81.2 60.5 300 92.2 82.2 91.0 80.8 83.8 61.8 350 93.3 83.7 92.2 82.9 85.8 62.9 400 94.0 84.8 92.6 84.5 87.3 63.8 450 94.7 85.8 93.7 85.8 88.6 64.5 500 95.0 86.6 94.2 86.9 89.6 65.1
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