摘要
现代雷达目标检测往往面临复杂多变的杂波环境,传统的基于模型驱动的恒虚警率(Constant False Alarm Rate,CFAR)检测器容易出现模型失配的问题,现有的基于数据驱动的有监督深度学习方法存在烦琐且昂贵的标签问题.针对上述问题,本文提出了一种基于深度无监督变分网络的杂波建模方法,该方法利用面向雷达回波高维分布特征学习的变分自编码器(Variational Auto-Encoder,VAE),针对雷达回波处理后的距离-多普勒谱,实现对复杂杂波分布的重构建模.首先,在VAE的无监督推理-生成构架中引入卷积神经网络(Convolutional Neural Network,CNN)和循环神经网络(Recurrent Neural Network,RNN),分别利用CNN网络的局部特征捕捉能力和RNN网络的时序相关性信息提取能力来实现对距离-多普勒谱的重构建模.其次,为了充分地捕获距离-多普勒谱中不同区域的杂波分布特征及二维时空信息,本文提出了一种基于时空变分Transformer的杂波建模方法,该方法将Transformer架构引入到所提的深度无监督杂波建模的变分网络中,借助Transformer网络的自注意力机制来捕获R-D谱数据的全局相关性.再次,为了充分挖掘不同场景下R-D谱的杂波分布特征及保留原始数据的二维时空信息,设计了开关机制和二维位置编码机制以匹配Transformer网络架构.最后,结合分布外(Out-Of-Distribution,OOD)检测策略,本文提出了一种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,重构似然表示无监督变分网络准确重构出输入样本的难易程度.重构似然越大,重构样本与输入样本越相似.因此,本文利用重构似然定义OOD分数,作为划分目标与杂波的依据,实现雷达目标检测任务.通过仿真数据验证,本文所提的无监督杂波建模方法能够实现对雷达距离-多普勒谱杂波分布的精细重构,且相比传统CFAR方法,当达到80%检测概率时,本文提出的方法所需信杂噪比(Signal to Clutter plus Noise Ratio,SCNR)优化了5.6 dB.
Modern radar target detection often faces complex and changeable clutter environments.Traditional modeldriven constant false alarm rate(CFAR)detectors are prone to model mismatch problems,and existing data-driven supervised deep learning methods require cumbersome and expensive label problems.In response to the above problems,this paper 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 network(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 Transformer.This method introduces the Transformer architecture into the proposed deep unsupervised clutter modeling variational network.Capture the global correlation of R-D spectral data by leveraging the self-attention mechanism of the Transformer network.In order to fully explore the clutter distribution characteristics of R-D spectra in different scenarios and retain the two-dimensional spatiotemporal information of the original data,a switching mechanism and a two-dimensional position encoding mechanism are designed to match the Transformer network architecture.Finally,combined with the out-ofdistribution(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 network 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 likelihood 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 probability reaches 80%,the signal to clutter plus noise ratio(SCNR)required by the method proposed in this paper The SCNR is optimized by 5.6 dB.
作者
刘要强
陈文超
施力行
田隆
王鹏辉
陈渤
LIU Yao-qiang;CHEN Wen-chao;SHI Li-xing;TIAN Long;WANG Peng-hui;CHEN Bo(National Key Laboratory of Radar Signal Processing,Xidian University,Xi’an,Shaanxi 710071,China;School of Electronic Engineering,Xidian University,Xi’an,Shaanxi 710071,China;School of Computer Science and Technology,Xidian University,Xi’an,Shaanxi 710071,China)
出处
《电子学报》
北大核心
2025年第8期2691-2706,共16页
Acta Electronica Sinica
基金
国家自然科学基金(No.6220010437,No.U21B2006)
雷达信号处理全国重点实验室基金(No.JKW202X0X,No.KGJ202401)。
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
radar target detection
unsupervised variational network
variational auto-encoder(VAE)
clutter modeling
range-Doppler spectrum
reconstruction likelihood
