Abstract
FDA-MIMO radar has good anti-active interference performance and has received extensive attention due to its distance dependence. However, there is still a lack of target and interference recognition methods for FDA-MIMO radar in complex environments. Therefore, in this paper, we propose a multi-channel feature extraction classification method based on the support vector machine (SVM), which realizes the classification of four active interferences and targets. In short, we classify the fine signal characteristics of different interference signals by extracting effective features and selecting efficient classifiers. Simulation results show that when the interference to noise ratio (INR) is greater than -15 dB, the recognition accuracy is greater than 0.95. It also shows that the proposed method can distinguish target and interference well in a variety of complex environments.
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