基于核主分量相關(guān)判別分析特征提取方法的目標(biāo)HRRP識(shí)別

李龍; 劉崢

doi:10.11999/JEIT170329

基于核主分量相關(guān)判別分析特征提取方法的目標(biāo)HRRP識(shí)別

doi: 10.11999/JEIT170329

李龍¹,
劉崢¹

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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2017-04-14
- 修回日期: 2017-07-10
- 刊出日期: 2018-01-19

Kernel Principal Component Correlation and Discrimination Analysis Feature Extraction Method for Target HRRP Recognition

LI Long¹,
LIU Zheng¹

摘要

摘要: 為有效提高雷達(dá)高分辨1維距離像目標(biāo)識(shí)別系統(tǒng)的總體性能，需要對(duì)目標(biāo)高分辨1維距離像進(jìn)行特征提取，以得到具有最小信息損失、高可分性且低維度的目標(biāo)特征，為實(shí)現(xiàn)該目的提出一種基于核主分量相關(guān)判別分析的特征提取算法。該算法基于目標(biāo)高分辨1維距離像的統(tǒng)計(jì)特性，通過(guò)對(duì)核主分量分析中核函數(shù)的選擇，實(shí)現(xiàn)對(duì)不同類型距離單元的特征提取。同時(shí)綜合線性判別分析與典型相關(guān)分析理論構(gòu)建新的準(zhǔn)則函數(shù)，以實(shí)現(xiàn)特征空間中類內(nèi)相關(guān)性與類間差異性最大化，同時(shí)減少目標(biāo)特征中的冗余信息。利用實(shí)測(cè)數(shù)據(jù)進(jìn)行實(shí)驗(yàn)，結(jié)果表明該方法提高了特征向量的可分性，降低了特征向量的維度，并且對(duì)該算法在不同強(qiáng)度雜波下的識(shí)別性能進(jìn)行了分析，實(shí)驗(yàn)結(jié)果表明，該方法可以有效的提高目標(biāo)高分辨1維距離像目標(biāo)識(shí)別系統(tǒng)的總體性能。
- 高分辨距離像目標(biāo)識(shí)別 /
- 特征提取 /
- 核主分量分析 /
- 線性判別分析 /
- 典型相關(guān)分析
Abstract: For radar High Resolution Range Profile (HRRP) automatic target recognition, the features should be extracted with sufficient target information, high discrimination, noise robustness, and low feature vector dimension. However, radar HRRP recognition suffers from insufficient amount of information and low discrimination feature, besides the radar recognition system also need the ability of real-time processing with low dimension. To obtain features with merits of low-dimension and high-discrimination, a novel feature extraction method is designed for radar high range resolution profile, namely Kernel Principal Component Correlation and Discrimination Analysis (KPCCDA). With the proposed method, the statistical characteristics of different scatter range cells can be effectively used by Kernel Principal Component Analysis (KPCA). And the within-class correlation and between- class discrimination are maximized with linear discrimination analysis and canonical correlation analysis used. Besides, the redundancy and dimensionality of the feature vectors are reduced, yielding a lowered computational complexity to meet the storage requirement in practical radar target recognition. Experimental results with measured data validate the efficiency of the proposed method.

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