基于ISVM-DS的红外多传感器融合识别方法

doi:10.12305/j.issn.1001-506X.2024.05.10

摘要/Abstract

摘要：

弹道中段目标为一个目标群, 包括弹头、诱饵、碎片等, 并且由于距离传感器较远, 红外成像为点目标, 可用信息较少, 因此单一的红外传感器往往难以满足识别要求, 需要融合多个传感器进行识别。针对红外多传感器的融合识别问题, 本文提出了基于增量支持向量机和D-S (increment support vector machine-Dempster-Shafer, ISVM-DS)证据理论的融合识别方法。首先, 训练多个波段传感器红外特征的支持向量数据描述(support vector data description, SVDD)模型, 生成壳向量并训练其ISVM模型; 接着, 采用ISVM模型的后验概率生成基本概率赋值(basic probability assignment, BPA); 最后, 利用D-S证据理论对多个证据的BPA进行融合, 输出分类结果。实验结果表明, 该方法能有效提高目标识别的准确性。

关键词: 弹道目标识别, 多传感器融合, Dempster-Shafer证据理论, 支持向量机

Abstract:

In the middle part of the ballistic trajectory, the target is a group of targets, including warheads, decoys, and fragments. Moreover, due to the long distance from the sensor, the infrared imaging of the target is a point target with less available information. Therefore, a single infrared sensor is often difficult to meet the recognition requirements, which means that multiple sensors need to be fused to complete the recognition task. In response to the fusion recognition problem of infrared multiple sensors, a fusion recognition method based on increment support vector machine-Dempster-Shafer (ISVM-DS) evidence theory is proposed. Firstly, the support vector data description (SVDD) model of infrared features of multiple band sensors is trained, and the shell vector is generated and the ISVM model is trained. Then the posterior probability of the ISVM model is used to generate basic probability assignment (BPA). Finally, the D-S evidence theory is used to fuse the BPA of multiple evidences and output classification results. Experimental results show that the proposed method can effectively improve the accuracy of target recognition.

Key words: ballistic target recognition, multi-sensor fusion, Dempster-Shafer (D-S) evidence theory, support vector machine (SVM)

中图分类号:

TP391.41

吴钇达, 王彩云, 王佳宁, 李晓飞. 基于ISVM-DS的红外多传感器融合识别方法[J]. 系统工程与电子技术, 2024, 46(5): 1555-1560.

Yida WU, Caiyun WANG, Jianing WANG, Xiaofei LI. Infrared multi-sensor fusion recognition method based on ISVM-DS[J]. Systems Engineering and Electronics, 2024, 46(5): 1555-1560.

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训练数据来源	识别结果
训练数据来源	高斯核贝叶斯	三层神经网络	本文算法
波段1	0.813 3	0.840 0	0.906 7
波段2	0.773 3	0.866 7	0.902 2
波段3	0.826 7	0.826 7	0.920 0
平均值	0.804 4	0.844 5	0.909 6

训练数据来源	KNN-DS	SVM-DS	本文算法
波段1 & 2	0.813 3	0.866 7	0.933 3
波段1 & 3	0.826 7	0.893 3	0.960 0
波段2 & 3	0.813 3	0.906 7	0.946 7
波段1、2、3	0.840 0	0.866 7	0.986 7
平均识别率	0.823 3	0.883 4	0.956 7

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