抗噪鲁棒性可分级的稀疏阵列频率和到达角估计
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摘要
为提高空时域欠采样下的入射信号的频率和到达角估计器的抗噪鲁棒性,本文提出从改善联合估计器的两个基本问题入手解决此难题.在阵元排列与配置方面,构造仅包含3个阵元的互素松弛阵列,并且根据面向鲁棒性余数系统要求配置阵元间距;在频率和到达角参数重构算法的设计方面,本文用面向鲁棒性余数系统的重构方法替换了中国余数定理重构算法,并对抗噪鲁棒性调节机理做了深入详细的分析.仿真结果表明:经过以上两方面根本性的改进,本文提出的抗噪鲁棒性可分级的联合估计器,在无需增加硬件复杂度和系统成本的前提下,无论在频率估计还是到达角估计方面,其抗噪鲁棒性的信噪比阈值改善均可达到9dB以上.因而在雷达、遥感等被动感知领域具有较广阔的应用前景.
To improve the antinoise robustness of frequency and direction of arrival in the temporal-spatial undersampling case, this paper presents two aspects of improvements. On one hand, in the configuration of sparse array arrangement,this paper constructs a relaxed coprime sparse array consisting of 3 sensors,whose element spacings are configured in terms of TRRNS( Towards Robustness in Residue Number System) reconstruction algorithm; On the other hand, in the design of recovery algorithm, the original CRT( Chinese Remainder Theorem) based algorithm is replaced by the TRRNS algorithm,from which the mechanism of the anti-noise robustness scalable adjustment will be derived and verified by numerical simulations.Compared to the original CRT based joint estimator, the proposed estimator at least achieves 9 dB improvement of the SNR threshold without increasing the hardware complexity and system cost,which presents vast applications in radar, remote sensing and other passive sensing fields.
To improve the antinoise robustness of frequency and direction of arrival in the temporal-spatial undersampling case, this paper presents two aspects of improvements. On one hand, in the configuration of sparse array arrangement,this paper constructs a relaxed coprime sparse array consisting of 3 sensors,whose element spacings are configured in terms of TRRNS( Towards Robustness in Residue Number System) reconstruction algorithm; On the other hand, in the design of recovery algorithm, the original CRT( Chinese Remainder Theorem) based algorithm is replaced by the TRRNS algorithm,from which the mechanism of the anti-noise robustness scalable adjustment will be derived and verified by numerical simulations.Compared to the original CRT based joint estimator, the proposed estimator at least achieves 9 dB improvement of the SNR threshold without increasing the hardware complexity and system cost,which presents vast applications in radar, remote sensing and other passive sensing fields.
引文
[1]Xu L,Li J,Stoica P. Target detection and parameter estimation for M IM O radar systems[J]. IEEE Transactions on Aerospace and Electronic Systems,2009,44(3):927-939.
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[15]刘亮,陶建武,黄家才.基于稀疏对称阵列的近场源定位[J].电子学报,2009,37(6):1307-1312.LIU Liang,TAO Jian-w u,HUANG Jia-cai. Near-field source localization based on sparse symmetric array[J].Acta Electronica Sinica,2009,37(6):1307-1312.(in Chinese)
[16]王彪,朱志慧,戴跃伟.基于具有时序结构的稀疏贝叶斯学习的水声目标DOA估计研究[J].电子学报,2016,44(3):693-698.WANG Biao,ZHU Zhi-hui,DAI Yue-w ei. Direction ofarrival estimation research for underw ater acoustic target based on sparse Bayesian learning w ith temporally correlated source vectors[J]. Acta Electronica Sinica,2016,44(3):693-698.(in Chinese)
[17]梁红,张恒.空时欠采样下多目标频率和方位联合估计新方法[J].西北工业大学学报,2012,30(5):694-698.LIANG Hong,ZHANG Heng. An effective method for joint estimation of frequency and DOA w ith sub-Nyquist spatial-temporal signals based on GRCRT for multiple numbers[J]. Journal of Northw estern Polytechnical University,2012,30(5):694-698.(in Chinse)
[18]Li X,Liang H,Xia X G. A robust Chinese remainder theorem w ith its applications in frequency estimation from undersampled w aveforms[J]. IEEE Transactions on Signal Processing,2009,57(11):4314-4322.
[19]黄翔东,刘明卓,等.单次空时域并行欠采样下的频率和到达角联合估计[J].物理学报,2017,66(18):188401.HUANG Xiang-dong,LIU M ing-zhuo,et al. Joint estimation of frequency and direction of arrival under the singleand-parallel spatial-temporal undersampling condition[J].Acta Physica Sinica,2017,66(18):188401.(in Chines)
[20]Wang W,Xia X G. A closed-form robust Chinese remainder theorem and its performance analysis[J]. IEEE Transactions on Signal Processing,2010,58(11):5655-5666.
[21]Xiao L,Xia X G,Huo H. Towards robustness in residue number systems[J]. IEEE Transactions on Signal Processing,2017,65(6):1497-1510.
[22]Qin S,Zhang Y D,Amin M G. Frequency diverse coprime arrays w ith coprime frequency offsets for multi-target localization[J]. IEEE Journal of Selected Topics in Signal Processing,2017,11(2):321-335.
[2] Rappaport T S. Wireless Communications:Principles and Practice[M]. Prentice Hall PTR New Jersy,1996.
[3] Li Y,Seshadri N,Ariyavisitakul S. Channel estimation for OFDM systems w ith transmitter diversity in mobile w ireless channels[J]. IEEE Journal on Selected Areas in Communications,1999,17(3):461-471.
[4]Gustafsson F,Gunnarsson F. Positioning using time-difference of arrival measurements[A]. Proceedings of the 2003IEEE International Conference on Acoustics,Speech,and Signal Processing[C]. Piscataw ay,NJ:IEEE,2003. 553-556.
[5] Poisel R. Electronic Warfare Target Location Methods[M]. Artech House,2012.
[6] Moffet A. Minimum-redundancy linear arrays[J]. IEEE Transactions on Antennas and Propagation,2003,16(2):172-175.
[7]Abajo F J,Garcia D. Colloquium:Light scattering by particle and hole arrays[J]. Review of M odern Physics,2009,79(4):1267-1290.
[8]Pal P,Vaidyanathan P P. Coprime sampling and the MUSIC algorithm[A]. Proceedings of the 2011 Digital Signal Processing and Signal Processing Education M eeting[C]. Piscataw ay,NJ:IEEE,2011. 289-294.
[9] Vaidyanathan P P,Pal P. Sparse sensing with co-prime samplers and arrays[J]. IEEE Transactions on Signal Processing,2011,59(2):573-586.
[10]Vaidyanathan P P,Pal P. Theory of sparse coprime sensing in multiple dimensions[J]. IEEE Transactions on Signal Processing,2011,59(8):3592-3608.
[11]Liu C L,Vaidyanathan P P. Remarks on the spatial smoothing step in coarray M USIC[J]. IEEE Signal Processing Letters,2015,22(9):1438-1442.
[12]Pal P,Vaidyanathan P P. Nested arrays:A novel approach to array processing w ith enhanced degrees of freedom[J].IEEE Transactions on Signal Processing,2010,58(8):4167-4181.
[13] Liu C L,Vaidyanathan P P. Super nested arrays:Linear sparse arrays w ith reduced mutual coupling-part I:Fundamentals[J]. IEEE Transactions on Signal Processing,2016,64(15):3997-4012.
[14] Liu C L,Vaidyanathan P P. Super nested arrays:Linear sparse arrays w ith reduced mutual coupling-part II:Highorder extensions[J]. IEEE Transactions on Signal Processing,2016,64(16):4203-4217.
[15]刘亮,陶建武,黄家才.基于稀疏对称阵列的近场源定位[J].电子学报,2009,37(6):1307-1312.LIU Liang,TAO Jian-w u,HUANG Jia-cai. Near-field source localization based on sparse symmetric array[J].Acta Electronica Sinica,2009,37(6):1307-1312.(in Chinese)
[16]王彪,朱志慧,戴跃伟.基于具有时序结构的稀疏贝叶斯学习的水声目标DOA估计研究[J].电子学报,2016,44(3):693-698.WANG Biao,ZHU Zhi-hui,DAI Yue-w ei. Direction ofarrival estimation research for underw ater acoustic target based on sparse Bayesian learning w ith temporally correlated source vectors[J]. Acta Electronica Sinica,2016,44(3):693-698.(in Chinese)
[17]梁红,张恒.空时欠采样下多目标频率和方位联合估计新方法[J].西北工业大学学报,2012,30(5):694-698.LIANG Hong,ZHANG Heng. An effective method for joint estimation of frequency and DOA w ith sub-Nyquist spatial-temporal signals based on GRCRT for multiple numbers[J]. Journal of Northw estern Polytechnical University,2012,30(5):694-698.(in Chinse)
[18]Li X,Liang H,Xia X G. A robust Chinese remainder theorem w ith its applications in frequency estimation from undersampled w aveforms[J]. IEEE Transactions on Signal Processing,2009,57(11):4314-4322.
[19]黄翔东,刘明卓,等.单次空时域并行欠采样下的频率和到达角联合估计[J].物理学报,2017,66(18):188401.HUANG Xiang-dong,LIU M ing-zhuo,et al. Joint estimation of frequency and direction of arrival under the singleand-parallel spatial-temporal undersampling condition[J].Acta Physica Sinica,2017,66(18):188401.(in Chines)
[20]Wang W,Xia X G. A closed-form robust Chinese remainder theorem and its performance analysis[J]. IEEE Transactions on Signal Processing,2010,58(11):5655-5666.
[21]Xiao L,Xia X G,Huo H. Towards robustness in residue number systems[J]. IEEE Transactions on Signal Processing,2017,65(6):1497-1510.
[22]Qin S,Zhang Y D,Amin M G. Frequency diverse coprime arrays w ith coprime frequency offsets for multi-target localization[J]. IEEE Journal of Selected Topics in Signal Processing,2017,11(2):321-335.