| 引用本文: | 杨延光,周智敏,宋千,等.基于VFGPIR联合特征的决策级加权融合检测方法.[J].国防科技大学学报,2008,30(6):107-113.[点击复制] |
| YANG Yanguang,ZHOU Zhimin,SONG Qian,et al.A Decision-level Weighting Fusion Detection Method Based on the Joint Multi-features of VFGPIR[J].Journal of National University of Defense Technology,2008,30(6):107-113[点击复制] |
|
| |
|
|
| 本文已被：浏览 7460次 下载 5904次 |
| 基于VFGPIR联合特征的决策级加权融合检测方法 |
| 杨延光1, 周智敏1, 宋千1, 初宁1, 金小三2 |
|
(1.国防科技大学 电子科学与工程学院，湖南 长沙 410073;2.海军705厂，广东 湛江 524016)
|
| 摘要: |
| 车载前视地表成像雷达(VFGPIR)探测浅埋目标时面临虚警率过高问题，可利用序列图像特征的联合检测解决这一问题。首先利用Fisher鉴别比(FDR)定量评估从单帧和序列图像中提取的单个特征的鉴别能力；然后针对单个最优序列特征无法满足探测指标要求提出一种基于决策级加权融合的多特征联合检测方法；最后利用接收机工作特性(ROC)曲线来验证所提方法的有效性。试验结果表明：序列特征比单帧图像特征具有更好的鉴别能力；所提方法性能优于单个最优序列特征、特征向量和多数票融合准则对应的检测结果，有望满足实际探雷应用需求。 |
| 关键词: 地表穿透成像雷达 地雷检测 决策级融合 Fisher鉴别比 接收机工作特性曲线 |
| DOI： |
| 投稿日期：2008-09-06 |
| 基金项目:国家部委重点资助项目 |
|
| A Decision-level Weighting Fusion Detection Method Based on the Joint Multi-features of VFGPIR |
| YANG Yanguang1, ZHOU Zhimin1, SONG Qian1, CHU Ning1, JIN Xiaosan2 |
|
(1.College of Electronic Science and Engineering, National Univ. of Defense Technology, Changsha 410073, China;2.705 Factory of Navy, Zhanjiang 524016, China)
|
| Abstract: |
| The vehicle-mounted forward-looking ground penetrating imaging radar (VFGPIR) is a feasible facility to detect shallow buried objects, but it faces a high false alarm rate. The combined detection, by using the sequence features, is proposed as the key to solve this problem. Firstly, we use the Fisher's discriminating ratio (FDR) to quantificationally evaluate the discriminating power of the individual feature component extracted from the single or serial images. Since the optimal individual sequence feature cannot satisfy the needs, a multi-features joint detection method based on the decision-level weighting fusion is presented in this paper. Finally, the receiver operating characteristic (ROC) curve is exploited to demonstrate the validity of the proposed method. Experimental results of the data show that the features extracted from the sequence images have better discriminating power, and the suggested method has a better detection performance than that of the optimal individual sequence feature, feature vector, and majority voting fusion rule. The approach is expected to satisfy the requirements of landmine detection in the practical application. |
| Keywords: ground penetrating imaging radar (GPIR) landmine detection decision-level fusion Fisher's discriminant ratio (FDR) receiver operating characteristic (ROC) curve |
|
|
|
|
|
