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首页> 外文会议>International Radar Conference >Synthesis of Micro-Doppler Signatures for Abnormal Gait using Multi-branch Discriminator with Embedded Kinematics
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Synthesis of Micro-Doppler Signatures for Abnormal Gait using Multi-branch Discriminator with Embedded Kinematics

机译：嵌入运动学的多分支鉴别器合成异常步态微多普勒信号。

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A key limiting factor in the depth, hence accuracy of deep neural networks (DNNs) designed for radar applications, is the meager amount of data typically available for training. Generative adversarial networks (GANs) have been proposed in many fields for the generation of synthetic data. It was shown, however, that when applied to micro-Doppler signature simulation, GANs suffer from performance degradation due to the generation of kinematically impossible samples. In this work, kinematic analysis of the micro-Doppler signature envelope is integrated as an additional branch in the discriminator network of a GAN to improve the kinematic fidelity of synthetic data when simulating abnormal gait signatures. Results show that the proposed multi-branch GAN network results in greater overlap in the feature space of synthetic abnormal gait samples with that of measured signatures for abnormal gait.
机译：深度的一个关键限制因素，因此是为雷达应用设计的深度神经网络（DNN）的准确性，是通常可用于训练的数据量很少。在许多领域中已经提出了生成对抗网络（GAN）来生成合成数据。然而，已经表明，当应用于微多普勒签名仿真时，由于运动学上不可能的样本的产生，GAN遭受性能下降。在这项工作中，微多普勒签名包络的运动学分析被集成为GAN鉴别器网络中的一个附加分支，以在模拟异常步态签名时提高合成数据的运动保真度。结果表明，所提出的多分支GAN网络在合成的异常步态样本的特征空间中与在测量的异常步态特征码的特征空间中具有更大的重叠。

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