电子与信息学报
電子與信息學報
전자여신식학보
JOURNAL OF ELECTRONICS & INFORMATION TECHNOLOGY
2014年
6期
1299-1306
,共8页
王磊%周乐囡%姬红兵%林琳
王磊%週樂囡%姬紅兵%林琳
왕뢰%주악닙%희홍병%림림
匹配追踪%雷达辐射源识别%稀疏表示%特征提取%监督分类
匹配追蹤%雷達輻射源識彆%稀疏錶示%特徵提取%鑑督分類
필배추종%뢰체복사원식별%희소표시%특정제취%감독분류
Matching Pursuit (MP)%Radar emitter identification%Sparse representation%Feature extraction%Supervised classification
匹配追踪(MP)的主要策略是通过每次迭代时选择一个局部最优解,从而逐步逼近原始信号。然而传统的MP系列算法进行原子匹配时,各类原子集间存在交集,从而影响了原子的表示能力以及相应的分类效果。基于此,该文提出一种适用于信号监督分类的匹配追踪新算法。其原子挑选的准则为:同类信号采用相同的原子集匹配,获取相同的类内表示结构;异类信号选择不同的原子集匹配,从而增强信号的类间差异。示例分析表明,使原子集间相互独立,能够减少异类信号间的共性因素,强化信号间的区分度,从而有利于提升分类识别效果。通过在标准图像库和实测雷达辐射源信号集上的实验表明,较之传统的 MP 系列方法,所提算法对噪声和遮挡具有更强的鲁棒性。
匹配追蹤(MP)的主要策略是通過每次迭代時選擇一箇跼部最優解,從而逐步逼近原始信號。然而傳統的MP繫列算法進行原子匹配時,各類原子集間存在交集,從而影響瞭原子的錶示能力以及相應的分類效果。基于此,該文提齣一種適用于信號鑑督分類的匹配追蹤新算法。其原子挑選的準則為:同類信號採用相同的原子集匹配,穫取相同的類內錶示結構;異類信號選擇不同的原子集匹配,從而增彊信號的類間差異。示例分析錶明,使原子集間相互獨立,能夠減少異類信號間的共性因素,彊化信號間的區分度,從而有利于提升分類識彆效果。通過在標準圖像庫和實測雷達輻射源信號集上的實驗錶明,較之傳統的 MP 繫列方法,所提算法對譟聲和遮擋具有更彊的魯棒性。
필배추종(MP)적주요책략시통과매차질대시선택일개국부최우해,종이축보핍근원시신호。연이전통적MP계렬산법진행원자필배시,각류원자집간존재교집,종이영향료원자적표시능력이급상응적분류효과。기우차,해문제출일충괄용우신호감독분류적필배추종신산법。기원자도선적준칙위:동류신호채용상동적원자집필배,획취상동적류내표시결구;이류신호선택불동적원자집필배,종이증강신호적류간차이。시례분석표명,사원자집간상호독립,능구감소이류신호간적공성인소,강화신호간적구분도,종이유리우제승분류식별효과。통과재표준도상고화실측뢰체복사원신호집상적실험표명,교지전통적 MP 계렬방법,소제산법대조성화차당구유경강적로봉성。
The main idea of Matching Pursuit (MP) is to get a local optimal solution by iteration, so as to gradually approach the original signal. To cope with the intersection of different atom sets, which may affect the classification performance of conventional MP methods, a new matching pursuit algorithm is proposed, which is suitable for supervised classification. The criterion for atoms selection consists of two parts. On one hand, by using the same atom set within the class, the intra-class structure of the similar signals is obtained for class-representation;on the other hand, by selecting the atom sets independently for every class, the discrimination ability for different classes could be further strengthened. The analysis on a toy example indicates that this scheme reduces the common factors between different classes and highlights the discrimination between signals, which may boost the performance of signal classification. Finally, the experiments on benchmark image databases and the measured radar emitter signals verify that the proposed algorithm achieves better robustness against noise and occlusion, compared with the convention MP-related methods.