Space object recognition method based on wideband radar RCS data

DOI:	10.11809/bqzbgcxb2024.07.037
Keywords:	radar target recognition; convolutional neural network; long short term memory network; inverse fourier transform; wavelet transform
Abstract:	This study proposes a radar RCS data target recognition technology based on time frequency analysis and hybrid neural network. Wavelet transform, inverse Fourier transform and logarithmic processing methods are used to process the two polarization broadband radar cross section (RCS) data to achieve efficient denoising and frequency domain analysis of the data, thereby obtaining a more accurate and clearer Dimensional range profile. Furthermore, a hybrid neural network structure is designed to process the obtained one dimensional range profile. The network structure comprehensively utilizes convolutional neural networks (CNN) to efficiently extract features, and uses long short term memory networks (LSTM) to capture temporal dependencies, thereby achieving efficient identification of two polarization data of radar RCS. In order to verify the effectiveness of this technology, a confirmatory experiment was conducted using the data set provided by a certain research institute, and compared with mainstream methods such as CNN, SVM, and MLP. Through parameter optimization and adjustment, the model achieved a recognition accuracy of 97.50%. This result shows that this method can make full use of time frequency information and successfully integrate local and global features, providing an efficient and accurate solution for radar RCS data target identification.
Published:	2024-07-26
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