A New Method for Parameter Estimation of Multicomponent LFM Signal based on Sparse Signal Representation

摘要：

Signal parameter estimation is a crucial issue in SAR/ISAR imaging,especially for multicomponent Linear Frequency Modulated (LFM)signal with single degree of freedom.A new method of parameter estimation based on sparse signal representation is presented in this paper,which expands signal on a set of over-complete basis.The method is analyzed and validated for performance through simulation,with three commonly used signal sparse representation algorithms compared,including BP,FOCUSS and Sparse Bayesian Learning.The result shows that Sparse Bayesian Learning performs better in sparse components than the other two algorithms,which can estimate signal parameters more efficiently.

关键词： Multicomponent LFM signal Single Degree of Freedom Parameter Estimation Sparse Signal Representation Sparse Bayesian Learning

作者: Sha Zhu Hongqiang Wang Xiang Li

作者单位: School of Electronic Science and Engineering National University of Defense Technology Changsha,Hunan 410073,China

会议类型: 国际会议

会议名称: 2008 IEEE International Conference on Onformation and Automation(IEEE 信息与自动化国际会议)

会议地点: 张家界

会议语种:英文

页码: 15-19

在线出版日期: 2008-06-20（万方平台首次上网日期，不代表论文的发表时间）