电子学报2025，Vol.53Issue(8)：2691-2706,16.DOI:10.12263/DZXB.20250370
基于深度无监督变分网络的杂波建模与雷达目标检测方法
Clutter Modeling and Radar Target Detection Method Based on Deep Unsupervised Variational Networks
摘要
Abstract
Modern radar target detection often faces complex and changeable clutter environments.Traditional model-driven constant false alarm rate(CFAR)detectors are prone to model mismatch problems,and existing data-driven super-vised deep learning methods require cumbersome and expensive label problems.In response to the above problems,this pa-per proposes a clutter modeling method based on deep unsupervised variational networks.This method utilizes a variational autoencoder for learning the high-dimensional distribution features of radar echoes to achieve the reconstruction modeling of complex clutter distributions for the range-doppler spectrum after radar echo processing.Firstly,convolutional neural net-work(CNN)and recurrent neural network(RNN)are introduced into the unsupervised inference-generation framework of the variational autoencoder.The reconstruction modeling of range-doppler spectra is achieved by respectively utilizing the local feature capture ability of CNN networks and the temporal correlation information extraction ability of RNN networks.To fully capture the clutter distribution characteristics and two-dimensional spatiotemporal information in different regions of the range-doppler spectrum,this paper proposes a clutter modeling method based on spatiotemporal variational Trans-former.This method introduces the Transformer architecture into the proposed deep unsupervised clutter modeling varia-tional network.Capture the global correlation of R-D spectral data by leveraging the self-attention mechanism of the Trans-former network.In order to fully explore the clutter distribution characteristics of R-D spectra in different scenarios and re-tain the two-dimensional spatiotemporal information of the original data,a switching mechanism and a two-dimensional po-sition encoding mechanism are designed to match the Transformer network architecture.Finally,combined with the out-of-distribution(OOD)detection strategy,this paper proposes a clutter modeling and radar target detection method based on deep unsupervised variational networks,and reconstructs the likelihood representation of the unsupervised variational net-work to accurately reconstruct the difficulty level of the input samples.The greater the reconstruction likelihood,the more similar the reconstructed sample is to the input sample.Therefore,the OOD score is defined by using the reconstructed like-lihood as the basis for dividing the target from clutter to achieve the radar target detection task.Verified by simulation data,the unsupervised clutter modeling method proposed in this paper can achieve fine reconstruction of the clutter distribution in the radar range-Doppler spectrum.Moreover,compared with the traditional CFAR method,when the detection probabili-ty reaches 80%,the signal to clutter plus noise ratio(SCNR)required by the method proposed in this paper The SCNR is op-timized by 5.6 dB.关键词
雷达目标检测/无监督变分网络/变分自编码器(VAE)/杂波建模/距离-多普勒谱/重构似然Key words
radar target detection/unsupervised variational network/variational auto-encoder(VAE)/clutter model-ing/range-Doppler spectrum/reconstruction likelihood分类
信息技术与安全科学引用本文复制引用
刘要强,陈文超,施力行,田隆,王鹏辉,陈渤..基于深度无监督变分网络的杂波建模与雷达目标检测方法[J].电子学报,2025,53(8):2691-2706,16.基金项目
国家自然科学基金(No.6220010437,No.U21B2006) （No.6220010437,No.U21B2006）
雷达信号处理全国重点实验室基金(No.JKW202X0X,No.KGJ202401) National Natural Science Foundation of China(No.6220010437,No.U21B2006) （No.JKW202X0X,No.KGJ202401）
National Key Laboratory of Radar Signal Processing Fund(No.JKW202X0X,No.KGJ202401) （No.JKW202X0X,No.KGJ202401）
