东篱科研大数据发现系统（DRDS）

位置：成果数据库 > 期刊 > 期刊详情页

一种新的随机PRI脉冲多普勒雷达无模糊MTD算法

ISSN号：2095-283X
期刊名称：雷达学报
时间：2012
页码：28-35
分类：TN958[电子电信—信号与信息处理;电子电信—信息与通信工程] TN957.52[电子电信—信号与信息处理;电子电信—信息与通信工程]
作者机构：[1]School of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China
相关基金：Foundation item： Project（61171133） supported by the National Natural Science Foundation of China; Project（11JJ1010） supported by the Natural Science Fund for Distinguished Young Scholars of Hunan Province, China; Project（61101182） supported by National Natural Science Foundation for Young Scientists of China
相关项目：反导系统中的雷达目标识别技术

关键词：合成孔径雷达, 散射模型, 稀疏表示, 雷达成像, 恢复技术, 基础, 归因, 贝叶斯学习, attributed scatter center model, sparse representation, sparse Bayesian learning, fast Bayesian matching pursuit, smoothed l0 norm, sparse reconstruction by separable approximation, fast iterative shrinkage-thresholding algorithm

中文摘要：

The sparse recovery algorithms formulate synthetic aperture radar(SAR) imaging problem in terms of sparse representation(SR) of a small number of strong scatters’ positions among a much large number of potential scatters’ positions,and provide an effective approach to improve the SAR image resolution.Based on the attributed scatter center model,several experiments were performed with different practical considerations to evaluate the performance of five representative SR techniques,namely,sparse Bayesian learning(SBL),fast Bayesian matching pursuit(FBMP),smoothed l0 norm method(SL0),sparse reconstruction by separable approximation(SpaRSA),fast iterative shrinkage-thresholding algorithm(FISTA),and the parameter settings in five SR algorithms were discussed.In different situations,the performances of these algorithms were also discussed.Through the comparison of MSE and failure rate in each algorithm simulation,FBMP and SpaRSA are found suitable for dealing with problems in the SAR imaging based on attributed scattering center model.Although the SBL is time-consuming,it always get better performance when related to failure rate and high SNR.

英文摘要：

The sparse recovery algorithms formulate synthetic aperture radar （SAR） imaging problem in terms of sparse representation （SR） of a small number of strong scatters＇ positions among a much large number of potential scatters＇ positions, and provide an effective approach to improve the SAR image resolution. Based on the attributed scatter center model, several experiments were performed with different practical considerations to evaluate the performance of five representative SR techniques, namely, sparse Bayesian learning （SBL）, fast Bayesian matching pursuit （FBMP）, smoothed 10 norm method （SL0）, sparse reconstruction by separable approximation （SpaRSA）, fast iterative shrinkage-thresholding algorithm （FISTA）, and the parameter settings in five SR algorithms were discussed. In different situations, the performances of these algorithms were also discussed. Through the comparison of MSE and failure rate in each algorithm simulation, FBMP and SpaRSA are found suitable for dealing with problems in the SAR imaging based on attributed scattering center model. Although the SBL is time-consuming, it always get better performance when related to failure rate and high SNR.

同期刊论文项目