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基于压缩感知的空时自适应动目标参数估计 被引量:10

Space Time Adaptive Parameter Estimation of Moving Target Based on Compressed Sensing
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摘要 该文针对空时自适应处理(Space-Time Adaptive Processing,STAP)中目标参数估计问题,提出一种基于压缩感知(Compressed Sensing,CS)技术的估计方法,该方法根据目标信号在空时域的稀疏特性,利用CS技术实现目标信号重构从而估计出目标参数。为了解决稀疏恢复有效性与参数估计精度之间的矛盾,该文构造较小维数的基字典以确保基字典中各原子向量之间相关性尽可能小,并将此时得到的目标参数作为粗估值;接着在以粗估结果为邻域的区间内进行局部寻优,得到精确的估计结果。仿真结果证实了所提方法的有效性。 In this paper, by exploiting the intrinsic sparsity of the moving target in the angle-Doppler domain, a new space time adaptive moving target parameter estimation algorithm is proposed, which uses the technique of sparse recovery to estimate space-time parameter of the moving target. To solve the contradiction between the successful of sparse recovery probability and the higher resolution, a small dictionary is selected to keep the coherence value between every two adjacent columns of the dictionary equal to minimize, and the parameter estimated from the above sparse recovery is regard as a rough result. To obtain a more precise result, a following match filter is applied to the local neighborhood of the obtained rough value. Effectiveness of the new method is verified via simulation examples.
出处 《电子与信息学报》 EI CSCD 北大核心 2013年第11期2714-2720,共7页 Journal of Electronics & Information Technology
基金 国家自然科学基金(61231017 61071194 U1233109) 国家科技支撑计划(2011BAH24B12) 中央高校基本科研业务费(ZXH2011C006)资助课题
关键词 机载雷达 动目标检测 空时自适应处理(STAP) 参数估计 压缩感知(CS) 匹配滤波 Airborne radar Moving target detection Space-Time Adaptive Processing (STAP) Parameter estimation Compressed Sensing (CS) Match filter
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  • 1DONOHO D L. Compressed sensing[ J]. IEEE Transactions on In- formation Theory, 2006, 52(4) : 1289 - 1306.
  • 2CANDES E J, ELDAR Y C, NEEDEL D, et al. Compressed sens- ing with coherent and redundant dictionaries [ J]. Applied and Com- putational Harmonic Analysis, 2011,31 (1) : 59 - 73.
  • 3OMER B, ELDAR Y C. Sub-Nyquist radar via Doppler focusing [ J]. IEEE Transactions on Signal Processing, 2014, 62(7) : 1796 - 1811.
  • 4RAZZAQUE M A, BLEAKLRY C, DONBSON S. Compression in wireless sensor networks: a survey and comparative evaluation [ J]. ACM Transactions on Sensor Networks, 2013, 10(1) : 5.
  • 5RAJA G, MICHAEL E. OMP with highly coherent dictionaries [ C]// Proceedings of the 2013 10th International Conference on Sampling Theory and Applications. Bremen: European Association for Signal Processing, 2003:9 - 12.
  • 6RAJA G, ELAD M. Greedy signal space methods for incoherence and beyond [ EB/OL]. [ 2014- 08- 01 ]. http://arxiv, org/pdf/ 1309. 2676v3. pdf.
  • 7TROPP J A, LASKA J N, DUARTE M F, et al. Beyond Nyquist: efficient sampling of sparse bandlimited signals [ J]. IEEE Transac- tions on Information Theory, 2010, 56(1) : 520 -544.
  • 8MISHALI M, ELDAR Y C. From theory to practice: sub-Nyquist sampling of sparse wideband analog signals [ J]. IEEE Joumal of Selected Topics in Signal Processing, 2010,4(2) : 375 -391.
  • 9MATUSIAK E, ELDAR Y C. Sub-Nyquist sampling of short pulses [ J]. IEEE Transactions on Signal Processing, 2012, 60(3) : 1134 - 1148.
  • 10ELDAR Y C, MISHALI M. Robust recovery of signals from a structured union of subspaces [ J]. IEEE Transactions on Informa- tion Theory, 2009, 55 (11) : 5302 - 5316.

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