基于几何绕射模型的多频带信号融合新方法  

A New Method of Multi-Band Radar Signal Fusion Based on GTD Model

作　　者：叶钒[1] 何峰[1] 朱炬波[1] 梁甸农[1] 

机构地区：[1]国防科技大学电子科学与工程学院,湖南长沙410073

出　　处：《信号处理》2010年第9期1361-1365,共5页Journal of Signal Processing

摘　　要：多频带雷达信号融合处理利用从不同频段获取的目标在一维谱域呈稀疏分布的雷达观测数据,通过信号级相干融合来提高目标散射中心参数估计精度和一维距离像的分辨能力。传统谱估计类融合方法的性能都受限于模型阶数估计。而多频带的稀疏分布,破坏了观测系统矩阵的互相干性度量,从而使得基追踪(基于l_1范数的稀疏表示)方法的全局最优解可能并不等于信号的真实稀疏表示。本文在GTD散射模型的基础上,提出了一种基于稀疏贝叶斯学习的融合方法,既避免了阶数估计,又克服了基追踪方法的缺陷。实验结果也表明了此方法的优越性。Using radar measurements from different frequency bands which are distributed sparsely in one dimensional spectrum, multi-band radar signal fusion can improve accuracy of estimation of radar target scattering model parameters and range resolution of the range profile by signal level＇ s coherent fusion. The performance of traditional fusion based on spectrum estimation is limited by estimation of scattering model order. Furthermore, owing to sparse distribution of multi-band, the mutual coherence of observation system matrix is destroyed, and the global optimal solution of Basis Pursuit （sparse representation based on l1-norm） may be unequal to real sparse representation of signal. Thus a new method of multi-band radar signal fusion based on sparse Bayesian learning is proposed in this paper based on GTD model. This method avoids the step of the model order estimation, and overcomes the limitation of Basis Pursuit in multi-band signal fusion. The experimental results also show the advantage of this method.

关 键 词：多频带雷达信号融合 GTD模型 稀疏贝叶斯学习 基追踪 

分 类 号：TN957[电子电信—信号与信息处理]