Abstract
In remote sensing, micro-Doppler signatures are widely used in moving target detection and automatic target recognition. However, since Doppler signatures are easily affected by the moving direction of the target, prior information of aspect angle is essential for spectral analysis. Thus, a micro-Doppler-based classifier is considered to be “angle-sensitive.” In this article, we propose an angle-insensitive classifier for the omnidirectional classification problem using the monostatic radar through a proposed new convolutional neural network. We further provide a sensible definition of “angle sensitivity,” and perform experiments on two data sets obtained through simulations and measurements. The results demonstrate that the proposed algorithm outperforms both feature-based and existing deep-learning-based counterparts, and resolve the issue of angle sensitivity in micro-Doppler-based classification.
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