This paper proposes a parity recognition of blade number and manoeuvre intention classification algorithm of rotor target based on the convolutional neural network (CNN) using micro Doppler features. Firstly, the time-frequency spectrograms are acquired from the radar echo by the short-time Fourier transform. Secondly, based on the obtained spectrograms, a seven-layer CNN architecture is built to recognize the blade-number parity and classify the manoeuvre intention of the rotor target. The constructed architecture contains a leaky rectified linear unit and a dropout layer to accelerate the convergence of the architecture and avoid over-fitting. Finally, the spectrograms of the datasets are divided into three different ratios, i.e., 20%, 33% and 50%, and the cross validation is used to verify the effectiveness of the constructed CNN architecture. Simulation results show that, on the one hand, as the ratio of training data increases, the recognition accuracy of parity and manoeuvre intention is improved at the same signal-to-noise ratio (SNR); on the other hand, the proposed algorithm also has a strong robustness: the accuracy can still reach 90.72% with an SNR of −6 dB.

中文翻译：

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基于微多普勒特征的CNN叶片数奇偶识别和转子目标操纵意图分类算法

提出了基于卷积神经网络（CNN）的微多普勒特征的转子目标叶片数奇偶识别和机动意图分类算法。首先，通过短时傅立叶变换从雷达回波中获取时频频谱图。其次，基于所获得的频谱图，构建了一个七层的CNN体​​系结构，以识别叶片数奇偶性并对转子目标的操纵意图进行分类。所构建的体系结构包含一个泄漏的整流线性单元和一个漏失层，以加快体系结构的融合并避免过度拟合。最后，将数据集的频谱图分为三个不同的比率，即20％，33％和50％，并且使用交叉验证来验证所构建的CNN体​​系结构的有效性。仿真结果表明，一方面，随着训练数据比例的增加，在相同的信噪比下，奇偶性和操纵意图的识别精度得到提高；另一方面，所提出的算法也具有很强的鲁棒性：在SNR为-6 dB的情况下，精度仍然可以达到90.72％。