基于信号空域稀疏性的阵列处理技术  

Spatial Sparsity-Based Array Signal Processing Techniques

作　　者：刘章孟[1] 周一宇[1] 

机构地区：[1]国防科学技术大学电子科学与工程学院,长沙410073

出　　处：《电子对抗》2013年第6期1-7,14,共8页Electronic Warfare

基　　金：国家自然科学基金（N0.61302141）

摘　　要：经过三十余年的发展完善，以子空间类方法为主体的超分辨阵列处理理论和技术体系始终难以较好地满足低信噪比、小样本等条件下的阵列测向需求，给此类环境中阵列测向系统效能的发挥造成了很大困难。针对这一问题，近年来稀疏重构技术逐步被引入阵列信号处理领域，通过对入射信号普遍具备的空域稀疏性先验加以利用，在低信噪比、小样本等条件下获得了显著提升的阵列测向性能。文章在归纳基于信号空域稀疏性的阵列处理技术的基本思想、模型和方法的基础上，对其发展趋势进行展望。During the last more than three decades, subspace-based methods have experienced a period of rapid development, and they form the foundation of the state-of-the-art theory and techniques in the area of superresolution array signal processing. However, the lack of those methods in adaptation to demanding scenarios, such as low signal-to-noise ratio ~SNR） and much limited snapshots, has greatly blocked the comprehensive adaptation of those methods and their widespread application in practical systems. The sparse reconstruction techniques have been introduced into this area to address the above-mentioned problems. The related methods are able to make use of the spatial sparsity of the incident signals, and have gained significant performance improvements over their subspacebased counterparts, especially in cases of low SNR and much limited snapshots. This paper first makes a review of the ideas, models and methodology of the spatial sparsity-based array processing techniques, and then predicts the developments of those techniques in the near future.

关 键 词：阵列测向 稀疏重构 贝叶斯稀疏学习 

分 类 号：TN911.7[电子电信—通信与信息系统]