基于子空间旋转变换的低复杂度波达角估计算法

闫锋刚; 齐晓辉; 刘帅; 沈毅; 金铭

doi:10.11999/JEIT150539

基于子空间旋转变换的低复杂度波达角估计算法

doi: 10.11999/JEIT150539

2.
(哈尔滨工业大学哈尔滨 150001) ②(哈尔滨工业大学(威海) 威海 264209)

基金项目:

国家自然基金(61501142)，山东省自然科学基金(ZR2014FQ003)，中国博士后科学基金(2015M571414)，中央高校基本科研业务费专项资金(HIT.NSRIF.2016102)

计量
- 文章访问数: 1622
- HTML全文浏览量: 174
- PDF下载量: 567
- 被引次数: 0
出版历程
- 收稿日期: 2015-05-07
- 修回日期: 2015-12-18
- 刊出日期: 2016-03-19

Low-complexity DOA Estimation via Subspace Rotation Technique

2.
(Harbin Institute of Technology, Harbin 150001, China)

Funds:

The National Natural Science Foundation of China (61501142), Shandong Provincial Natural Science Foundation (ZR2014FQ003), China Postdoctoral Science Foundation (2015M571414), The Fundamental Research Funds for the Central Universities (HIT.NSRIF.2016102)

摘要

摘要: 多重信号分选(MUltiple SIgnal Classification, MUSIC)算法是波达方向(Direction-Of-Arrival, DOA)估计的最重要算法之一，但庞大的计算量使其工程实用性大打折扣。为降低MUSIC的计算量，该文基于子空间旋转(Subspace Rotation Technique, SRT)变换思想提出了一种高效改进算法，即SRT-MUSIC算法。SRT-MUSIC利用秩亏特性对噪声子空间矩阵按行分块并以旋转变换得到降维噪声子空间，进而基于该降维噪声子空间与导向矢量的正交性构造空间谱估计信号DOA。理论分析表明：SRT-MUSIC能有效避免空间谱搜索中的冗余运算，从而成倍降低算法的计算量。对于大阵元、少信号情况，所提算法计算效率优势更为明显。仿真实验证明了SRT-MUSIC的有效性和高效性。
- 波达方向估计 /
- 多重信号分选 /
- 噪声子空间旋转 /
- 低复杂度算法
Abstract: The MUltiple SIgnal Classification (MUSIC) algorithm is one of the most important techniques for Direction-Of-Arrival (DOA) estimate. However, this method is found expensive in practical applications, due to the heavy computational cost involved. To reduce the complexity, a novel efficient estimator based on Subspace Rotation Technique (STR) is proposed. The key idea is to divide the noise subspace matrix along its row direction into two sub-matrices, and perform STR to get a new rotated sub-noise subspace with reduced dimensions. As this rotated sub-noise subspace is also orthogonal to the signal subspace, a new cost function is finally derived to estimate DOAs. Theoretical analysis indicates that redundancy computations in spectral search are efficiently avoided by the proposed method as compared to MUSIC, especially in scenarios where large numbers of sensors are applied to locate small numbers of signals. Simulation results verify the effectiveness and efficiency of the new technique.
- Direction-Of-Arrival (DOA) estimate /
- MUltiple SIgnal Classification (MUSIC) /
- Noise subspace rotation /
- Low-complexity algorithm

HTML全文

参考文献(20)

SCHMIDT R O. Multiple emitter location and signal parameter estimation[J]. IEEE Transactions on Antennas Propagation, 1986, 34(3): 276-280.

GU J F, ZHU W P, and SWAMY M N S. Joint 2-D DOA estimation via sparse L-shaped array[J]. IEEE Transactions on Signal Processing, 2015, 63(5): 1171-1182.

毛琳琳, 张群飞, 黄建国, 等. 基于互相关协方差矩阵的改进多重信号分类高分辨波达方位估计方法[J]. 电子与信息学报, 2015, 37(8): 1886-1891. doi: 10.11999/JEIT141208.

MAO L L, ZHANG Q F, HUANG J G, et al. Improved multiple signal classification algorithm for direction of arrival estimation based on covariance matrix of cross-correlation[J]. Journal of Electronics Information Technology, 2015, 37(8): 1886-1891. doi: 10.11999/JEIT141208.

LIU Z M and GUO F C. Azimuth and elevation estimation with rotating long-baseline interferometers[J]. IEEE Transactions on Signal Processing, 2015, 63(9): 2405-2419.

REDDY W, MUBEEN M and NG B P. Reduced- complexity super-resolution DOA estimation with unknown number of sources[J]. IEEE Signal Processing Letters, 2015, 22(6): 772-776.

ROEMER F, et al. Analytical performance assessment of multidimensional matrix- and tensor-based ESPRIT-type algorithms[J]. IEEE Transactions on Signal Processing, 2014, 62(10): 2611-2625.

YAN F G, SHEN Y, and JIN M. Fast DOA estimation based on a split subspace decomposition on the array covariance matrix[J]. Signal Processing, 2015, 115(10): 1-8.

闫锋刚, 王军, 沈毅, 等. 基于半实值Capon的高效波达方向估计算法[J]. 电子与信息学报, 2015, 37(4): 811-816. doi: 10.11999/JEIT141034.

YAN F G, WANG J, SHEN Y, et al. Efficient direction- of-arrival estimation based on semi-real-valued capon[J]. Journal of Electronics Information Technology, 2015, 37(4): 811-816. doi: 10.11999/JEIT141034.

CHENG Q, HUANG L, and SO H C. Improved unitary root- MUSIC for DOA estimation based on pseudo-noise resampling[J]. IEEE Signal Processing Letters, 2014, 21(2): 140-144.

蔡晶晶, 等. 强约束优化降维MUSIC二维DOA估计[J]. 电子与信息学报, 2014, 36(5): 113-118. doi: 10.3724/SP.J.1146. 2013.01127.

CAI J J, et al. Two-dimensional DOA estimation using reduced-dimensional MUSIC algorithm with strong- constraint optimization[J]. Journal of Electronics Information Technology, 2014, 36(5): 113-118. doi: 10.3724/SP.J.1146. 2013.01127.

HUA G, et al. Efficient two dimensional direction finding via auxiliary-variable manifold separation technique for arbitrary array structure[C]. IEEE International Conference on Communication Problem-solving (ICCP), Beijing, 2014, 532-537.

RUBSAMEN M and GERSHMAN A B. Direction- of-arrival estimation for nonuniform sensor arrays: from manifold separation to Fourier domain MUSIC methods[J]. IEEE Transactions on Signal Processing, 2009, 57(2): 588-599.

YAN F G, JIN M, LIU S, et al. Real-valued MUSIC for efficient direction estimation with arbitrary array geometries [J]. IEEE Transactions on Signal Processing, 2014, 62(6): 1548-1560.

GOLUB G H and ChARES VAN LOAN H. Matrix Computations[M]. Baltimore, MD: The Johns Hopkins University Press, 1996: 238-246.

XU G and KAILATH T. Fast subspace decomposition[J]. IEEE Transactions on Signal Processing, 199, 42(3): 539-551.

REN Q S and WILLIS A J. Fast root MUSIC algorithm[J]. Electronics Letters, 1997, 33(6): 450-451.

ZHANH Q T. Probability of resolution of the MUSIC algorithm[J]. IEEE Transactions on Signal Processing, 1994, 43(4): 978-987.

施引文献

资源附件(0)

访问统计

计量

文章访问数: 1622
HTML全文浏览量: 174
PDF下载量: 567
被引次数: 0

姓名
邮箱
手机号码
标题
留言内容
验证码

留言板

基于子空间旋转变换的低复杂度波达角估计算法

doi: 10.11999/JEIT150539

计量

Low-complexity DOA Estimation via Subspace Rotation Technique

计量

目录

留言板

基于子空间旋转变换的低复杂度波达角估计算法

doi: 10.11999/JEIT150539

计量

出版历程

Low-complexity DOA Estimation via Subspace Rotation Technique

计量

出版历程

目录