概率数据关联是多源信息融合算法中的关键问题，本文主要对基于雷达和电子支援措施(ESM)双传感器融合的数据关联问题展开研究.由于雷达和ESM传感器方位角数据分布近似相同，可以通过对ESM数据的分析得到判别函数，使用相应的判决规则对点迹和航迹进行关联，这本质上可以看作是一个模式识别问题.本文考虑到支持向量机(SVM)模型在模式分类中的良好表现，建立了基于SVM的雷达ESM系统的点迹-航迹关联模型，使用ESM传感器航迹数据训练SVM模型，对雷达点迹数据进行分类，获得关联结果.最终模拟结果表明：与经典的多假设跟踪算法相比，所提出的算法可有效提高关联准确率.
Probabilistic data association is an important issue in multi-source information fusion algorithms. The data association problem based on radar and electronic support measurement (ESM) sensors is mainly discussed in this paper. As the azimuthal data of radar and ESM sensors have approximately the same distribution, the discriminant function can be obtained through the analysis of ESM data, and the corresponding decision rules can be used to associate the dots and tracks. The association issue can be essentially regarded as a pattern recognition problem. In this paper, considering the good performance of support vector machine(SVM) in pattern classification, we establish a dotting and tracking association model for radar ESM systems based on SVM algorithm. We train the SVM model with ESM data, and classify the radar data to acquire association result. Finally, the simulation results show that the association accuracy can be effectively improved compared with the classical multiple hypothesis tracking algorithm.
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