Radar emitter classification using self-organising Neural Network models
Radar emitter classification using self-organising Neural Network models
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DOI：
10.1109/amta.2008.4763033
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发表时间：
2008-11
期刊：
影响因子：
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通讯作者：
L. Anjaneyulu;N.S. Murthy;N.V.S.N. Sarma
中科院分区：
文献类型：
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作者：
L. Anjaneyulu;N.S. Murthy;N.V.S.N. Sarma
本文提出了一种基于模糊ART和ARTMAP神经网络的雷达发射源识别和分类技术。 RF、PW、PRI、到达方向（DOA）等雷达发射器参数被作为网络的输入。该网络使用发射器类型的可用数据进行训练。训练后，网络用于通过将发射器的参数作为神经网络的输入来识别发射器类型。进行了多次仿真，仿真结果表明该网络能够快速、准确地识别和分类发射器类型。
This paper presents a radar emitter identification and classification technique based on Fuzzy ART and ARTMAP Neural Networks. The radar emitterpsilas parameters of RF, PW, PRI, Direction of Arrival(DOA) etc., are taken as inputs for the networks. The network is trained with the available data of the emitter types. After training, the network is used to identify the emitter type by applying the parameters of the emitter as inputs to the neural network. A number of simulations are carried out and the simulated results show that the network quickly and accurately identify and classify the emitter types.