|Table of Contents|

Optimal Waveform Design for Compressive Sensing Radar

《南京理工大学学报》（自然科学版）[ISSN:1005-9830/CN:32-1397/N]

Issue:
2011年04期
Page:
519-524
Research Field:
Publishing date:

Info

Title:
Optimal Waveform Design for Compressive Sensing Radar
Author(s):
HE Ya-peng; ZHU Xiao-hua; ZHUANG Shan-na; WANG Ke-rang
School of Electronic Engineering and Optoelectronic Technology，NUST，Nanjing 210094，China
Keywords:
compressive sensing radars;  waveform design;  sensing matrix;  coherence;  simulated annealing
PACS:
TN958. 8
DOI:
-
Abstract:
To solve the problem of waveform optimization for compressive sensing radar( CSR) ，an optimized method for CSR waveform design through minimizing the coherence of the sensing matrix is proposed here． The system model of CSR is established and the objective function of the waveform optimization for minimizing the coherence of the sensing matrix is presented． The simulated annealing ( SA) algorithm is employed to find the optimal solution to the objective function taking polyphase coded signal as an example． The optimized waveform can effectively reduce the coherence of the corresponding sensing matrix，and thus improve the accuracy and robustness of the CSR target information extraction． The computer simulation shows that the optimized waveform significantly reduces the coherence of the sensing matrix compared with traditional radar waveforms，and hence confirms the effectiveness of this proposed method．

References:

［1］ 石光明，刘丹华，高大化，等． 压缩感知理论及其研 究进展［J］． 电子学报， 2009， 37( 5) : 1071－1081．
[2] Candès E J，Romberg J，Tao T． Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information［J］． IEEE Transactions on Information Theory， 2006， 52( 2) : 489－509．
[3] Candès E J，Romberg J，Tao T． Stable signal recovery from incomplete and inaccurate measurements ［J］． Communications on Pure and Applied Mathematics， 2006， 59( 8) : 1207－1223．
[4] Candès E J． Compressive sampling［A］． Proceedings of ICM2006［C］． Madrid，Spain: Association International Congress of Mathematicians， 2006: 1433－1452．
[5] Donoho D L． Compressed sensing ［J ］． IEEE Transactions on Information Theory， 2006， 52( 4) : 1289 －1306．
[6] Baraniuk R， Steeghs P． Compressive radar imaging［A］． Proceedings of IEEE International Radar Conference ［C］． Boston，USA: IEEE Press， 2007: 128－133．
[7] Herman M A，Strohmer T． High-resolution radar via compressed sensing［J］． IEEE Transactions on Signal Processing， 2009， 57( 6) : 2275－2284．
[8] 余慧敏，方广有． 压缩感知理论在探地雷达三维成 像中的应用［J］． 电子与信息学报，2010，32( 1) : 12 －16．
[9] 宋琳，曹吉海． 基于随机滤波的雷达信号采样和目 标重建方法［J］． 科技导报， 2008， 26( 13) : 64－67．
[10] Shi G M，Chen X Y，Qi F， et al． UWB echo signal detection with ultra-low rate sampling based on compressed sensing［J］． IEEE Transactions on Circuits and Systems II: Express Briefs， 2008， 55( 4) : 379－383．
[11] Tello A M，López-Dekker F，Mallorquí J J． A novel strategy for radar imaging based on compressive sensing［J］． IEEE Transactions on Geoscience and Remote Sensing， 2010， 48( 9) : 1－11．
[12] Subotic N S，Thelen B，Cooper K，et al． Distributed radar waveform design based on compressive sensing considerations［A］． Proceedings of IEEE International Radar Conference［C］． Rome， Italy: IEEE Press， 2008: 1－6．
[13] Donoho D L，Elad M，Temlyakov V N． Stable recovery of sparse overcomplete representations in the presence of noise［J］． IEEE Transactions on Information Theory， 2006， 52( 1) : 6－18．
[14] Deng H． Synthesis of binary sequences with good autocorrelation and crosscorrelation properties by simulated annealing［J］． IEEE Transactions on Aerospace and Electronic Systems， 1996， 32( 1) : 98－107．
[15] 张宇，王建新，孙锦涛． MIMO 雷达的相位编码信号 设计［J］． 兵工学报， 2010， 31( 1) : 109－112．
[16] Levanon N，Mozeson E． Radar signal［M］． New York: John Wiley ＆ Sons， 2004: 297－301．

Memo

Memo:
-
Last Update: 2012-10-23