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机载火控雷达空空工作状态识别研究
引用本文:马珂,毕大平.机载火控雷达空空工作状态识别研究[J].雷达科学与技术,2021,19(6):697-703.
作者姓名:马珂  毕大平
作者单位:国防科技大学电子对抗学院,安徽合肥230037
摘    要:为有效解决机载火控雷达空空工作模式反推识别难度大的问题,从非合作方的角度提出空空工作状态的概念,使用DS证据理论对8种工作状态进行识别。首先,基于对空空工作状态的特征分析设计了信号波形模拟器;其次,构建了包含单特征参数、多特征参数融合和多周期融合的分布式DS证据理论识别网络;最后,使用信号波形模拟器仿真的信号进行了随机样本测试。仿真结果显示,识别网络能够对8种工作状态进行有效区分,识别准确率达到92%以上,证明了该方法的合理性和实用性。

关 键 词:空空工作状态  DS证据理论  特征参数集  区间相似度

Research on Air-to-Air Working State Recognition of Airborne Fire Control Radar
MA Ke,BI Daping.Research on Air-to-Air Working State Recognition of Airborne Fire Control Radar[J].Radar Science and Technology,2021,19(6):697-703.
Authors:MA Ke  BI Daping
Abstract:To effectively solve the problem of air-to-air working mode identification of airborne fire control radar, the concept of air-to-air working state is proposed from the perspective of non-cooperative partner. The DS evidence theory is used to identify eight working states. Initially, a signal waveform simulator is designed based on the characteristic analysis of air-to-air working state. Sequentially, a distributed DS evidence theory identification network including single feature parameter, multi-feature parameter fusion and multi-period fusion is constructed. Eventually, the signal simulated by signal waveform simulator is tested with random samples. The simulation results show that the recognition network proposed can effectively distinguish the eight working states, and the recognition accuracy is more than 92%, which proves the rationality and practicability of the proposed method.
Keywords:air-to-air working state  DS evidence theory  feature parameter set  interval similarity
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