Detection Algorithm for Small and Weak Target Based on LSSVM Combining Spectral Dimension Information

doi:10.3969/j.issn.1009-086x.2018.04.014

Abstract

Abstract: The detection of the space-based infrared warning system usually dwells on the infrared images processing. A new detection method based on least squares support vector machine (LSSVM) combining spectral dimension information is discussed by analyzing the spectral dimension data and the low signal to noise ratio. Artificial bee colony algorithm is used to optimize the kernel function and regularization parameters, using F measure function as the fitness value. A LSSVM classifier is designed under small training set conditions, using the plume infrared spectrum of four typical missile types as training samples. The detection effect shows that the model of artificial bee colony algorithm optimization achieves better results compared with grid search algorithm, particle swarm optimization and genetic algorithm especially under low signal to noise ratio.

Key words: infrared dim and small targets, missile plume, least squares support vector machine, parameter optimization, artificial bee colony algorithm, target detection

摘要： 现有天基红外导弹预警系统对目标的检测侧重于对红外图像的处理。从光谱维数据分析角度出发,结合红外弱小目标图像信噪比低的特点,提出了一种利用最小二乘向量机(LSSVM)结合目标光谱维信息检测弱小目标的方法,并以F测度函数为适应度函数,利用人工蜂群算法(ABC)对最小二乘向量机的正则化参数和核函数参数进行优化。应用小样本训练数据建立了LSSVM检测器,以4型典型导弹目标尾焰红外辐射特征谱数据作为训练样本,比较了ABC算法、网格搜索(grid search)算法、粒子群(PSO)算法和遗传(GA)算法。实验结果表明,在低信噪比条件下,由ABC优化的LSSVM模型能获得更好的检测性能。

关键词: 红外弱小目标, 导弹尾焰, 最小二乘支持向量机, 参数优化, 人工蜂群算法, 目标检测

CLC Number:

TJ76
TN215

KANG Hong-xia, HUANG Shu-cai, HUANG Wen-long, WU Jian-feng. Detection Algorithm for Small and Weak Target Based on LSSVM Combining Spectral Dimension Information[J]. Modern Defense Technology, 2018, 46(4): 86-91.

康红霞, 黄树彩, 黄文龙, 吴建峰. 最小二乘向量机结合光谱维信息检测弱小目标[J]. 现代防御技术, 2018, 46(4): 86-91.

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