1 |
闫锋刚, 沈毅, 刘帅, 等. 高效超分辨波达方向估计算法综述[J]. 系统工程与电子技术, 2015, 37 (7): 1465- 1475.
|
|
YAN F G , SHEN Y , LIU S , et al. Overview of efficient algorithms for super resolution DOA estimates[J]. Systems Engineering and Electronics, 2015, 37 (7): 1465- 1475.
|
2 |
田航. MUSIC算法性能研究综述[J]. 科技资讯, 2019, 17 (27): 5- 6.
|
|
TIAN H . Overview of MUSIC algorithm performance[J]. Science & Technology Information, 2019, 17 (27): 5- 6.
|
3 |
吴晓欢. 基于稀疏表示的波达方向估计理论与方法研究[D]. 南京: 南京邮电大学, 2017.
|
|
WU X H. Sparse representation based theory and methods for direction-of-arrival estimation[D]. Nanjing: Nanjing University of Posts and Telecommunications, 2017.
|
4 |
刁弘扬, 胡洲勇, 禹永植. 一种改进广义正交匹配追踪的DOA估计方法[J]. 应用科技, 2020, 47 (4): 54- 58.
|
|
DIAO H Y , HU Z Y , YU Y Z . An improved generalized orthogonal matching pursuit method used in the direction of arrival estimation[J]. Applied Science and Technology, 2020, 47 (4): 54- 58.
|
5 |
TIBSHIRANI R . Regression shrinkage and selection via the Lasso: a retrospective[J]. Journal of the Royal Statistical Society Series B—Statistical Methodology, 2011, 73 (2): 267- 288.
|
6 |
LING Y , GAO H T , ZHOU S , et al. Robust sparse Bayesian learning-based off-grid DOA estimation method for vehicle localization[J]. Sensors, 2020, 20 (1): 302.
doi: 10.3390/s20010302
|
7 |
ZHANG X Y, HUO K, LIU Y, et al. Direction of arrival estimation via joint sparse Bayesian learning for bi-static passive radar[C]//Proc. of the IEEE International Conference on Signal, Information and Data Proccessing, 2019.
|
8 |
LUO M, GUO Q H, HUANG D F, et al. Sparse Bayesian learning based on approximate message passing with unitary transformation[C]//Proc. of the IEEE VTS Asia Pacific Wireless Communications Symposium, 2019.
|
9 |
MAO Y W , LUO M , GAO D W , et al. Low complexity DOA estimation using AMP with unitary transformation and iterative refinement[J]. Digital Signal Processing, 2020, 106, 102800.
doi: 10.1016/j.dsp.2020.102800
|
10 |
DAI J S , BAO X , XU W C , et al. Root sparse Bayesian learning for off-grid DOA estimation[J]. IEEE Signal Processing Letters, 2017, 24 (1): 46- 50.
doi: 10.1109/LSP.2016.2636319
|
11 |
HURI N, FEDER M. Selecting the LASSO regularization parameter via Bayesian principles[C]//Proc. of the IEEE International Conference on the Science of Electrical Engineering, 2016.
|
12 |
RANGAN S. Generalized approximate message passing for estimation with random linear mixing[EB/OL]. [2021-03-29]. http://arXiv.org/abs/1010.5141.
|
13 |
RANGAN S, SCHNITER P, FLETCHER A. On the convergence of approximate message passing with arbitrary matrices[C]//Proc. of the IEEE International Symposium on Information Theory, 2014.
|
14 |
DAI J , SO H C . Sparse Bayesian learning approach for outlier resistant direction of arrival estimation[J]. IEEE Trans.on Signal Processing, 2018, 66 (3): 744- 756.
doi: 10.1109/TSP.2017.2773420
|
15 |
LIU Z M , HUANG Z T , ZHOU Y Y . An efficient maximum likelihood method for direction-of-arrival estimation via sparse bayesian learning[J]. IEEE Trans.on Wireless Communications, 2012, 11 (10): 1- 11.
doi: 10.1109/TWC.2012.090312.111912
|
16 |
WIPF D P , RAO B D . An empirical Bayesian strategy for solving the simultaneous sparse approximation problem[J]. IEEE Trans.on Signal Processing, 2007, 55 (7): 3704- 3716.
doi: 10.1109/TSP.2007.894265
|
17 |
GERSTOFT P , MECKLENBRAUKER C F , XENAKI A , et al. Multisnapshot sparse Bayesian learning for DOA[J]. IEEE Signal Processing Letters, 2016, 23 (10): 1469- 1473.
doi: 10.1109/LSP.2016.2598550
|
18 |
VILA J P , SCHNITER P . Expectation maximization Gaussian mixture approximate message passing[J]. IEEE Trans.on Signal Processing, 2013, 61 (19): 4658- 4672.
doi: 10.1109/TSP.2013.2272287
|
19 |
MALIOUTOV D , CETIN M , WILLSKY A S . A sparse signal reconstruction perspective for source localization with sensor arrays[J]. IEEE Trans.on Signal Processing, 2005, 53 (8): 3010- 3012.
doi: 10.1109/TSP.2005.850882
|