Citation: | Yun Wu, Xiukun Li, Zhimin Cao. Effective DOA Estimation Under Low Signal-to-Noise Ratio Based on Multi-Source Information Meta Fusion[J].JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2021, 30(4): 377-396.doi:10.15918/j.jbit1004-0579.2021.052 |
[1] |
I. Bekkerman and J. Tabrikian, “Target detection and localization using MIMO radars and sonars, ”
IEEE Trans. on Signal Process, vol. 54, no. 10, pp. 3873-3883, 2006.
|
[2] |
Z. Xia, X. K. Li, and X. X. Meng, “High resolution time-delay estimation of underwater target geometric scattering, ”
Applied Acoustics, vol. 114, pp. 111-117, 2016.
|
[3] |
X. Guo and H. Sun. “Compressive sensing for target DOA estimation in radar, ” in
2014 International Radar Conference, Lille, 2014, pp. 1-5.
|
[4] |
S. G. Shi, Y. Li, Z. R. Zhu, and J. Shi, “Real-valued robust DOA estimation method for uniform circular acoustic vector sensor arrays based on worst-case performance optimization, ”
Applied Acoustics, vol. 148, pp. 495-502, 2019.
|
[5] |
H. W. Chen and J. W. Zhao, “Coherent signal-subspace processing of acoustic vector sensor array for DOA estimation of wideband sources, ”
Signal Process, vol. 85, pp. 837-847, 2019.
|
[6] |
A. F. Liu and D. S. Yang, “Augmented subspace MUSIC method for DOA estimation using acoustic vector sensor array, ”
IET Radar, Sonar & Navigation, vol. 13, no. 6, pp. 969-975, 2018.
|
[7] |
S. B. Sun and X. Y. Zhang, “Underwater acoustical localization of the black box utilizing single autonomous underwater vehicle based on the second-order time difference of arrival, ”
IEEE Journal of Oceanic Engineering, vol. 45, no. 4, pp. 1268-1279, 2020.
|
[8] |
E. Dubrovinskaya, P. Casari, V. Kebkal, K. Oleksiy, and K. Konstantin, “Underwater localization via wideband direction-of-arrival estimation using acoustic arrays of arbitrary shape, ”
Sensors, vol. 20, no. 14, pp. 3862, 2020.
|
[9] |
T. Kikuchi, T. Yamaoka, and N. Hamada, “Microphone array system with DOA estimation by using harmonic structure of speech signals, ” IEICE Technical Report DSP98-164, 1999.
|
[10] |
V. V. Reddy, A. W. H. Khong, and B. P. Ng, “Unambiguous speech DOA estimation under spatial aliasing conditions, ”
IEEE/ACM Transactions on Audio Speech & Language Processing, vol. 22, no. 12, pp. 2133-2145, 2014.
|
[11] |
M. Matsuo, Y. Hioka, and N. Hamada, “Estimation DOA of multiple speech signals by improved histogram mapping method, ”
International Workshop on Acoustic Echo and Noise Control, vol. 1, pp. 129-132, 2005.
|
[12] |
S. Q. Wang and J. Yang, “Decentralized acoustic source localization with unknown source energy in a wireless sensor network, ”
Meas, Sci. Technol, vol. 18, no. 12, pp. 3768, 2007.
|
[13] |
L. Wan and G. Han, “Distributed parameter estimation for mobile wireless sensor network based on cloud computing in battlefield surveillance system, ”
IEEE Access, vol. 3, pp. 1729-1739, 2015.
|
[14] |
J. Capon, “High resolution frequency-wavenumber spectrum analysis, ”
Proc. of the IEEE, vol. 57, no. 8, pp. 1408-1418, 1969.
|
[15] |
C. J. Lam and C. S. Andrew, “Bayesian beamforming for DOA uncertainty: Theory and implementation, ”
IEEE Trans. on Signal Processing, vol. 54, no. 11, pp. 4435-4445, 2006.
|
[16] |
Y. P. Liu and Q. Wan, “Sidelobe suppression for capon beamforming with mainlobe to sidelobe power ratio maximization, ”
IEEE Antennas&
Wireless Propagation Letters, vol. 11, no. 3, pp. 1218-1221, 2011.
|
[17] |
R. O. Schmidt, “A signal subspace approach to multiple emitter location and spectral estimation, ” Ph. D. Dissertation, Stanford University, Stanford, CA, 1981.
|
[18] |
B. D. Rao and K. V. S. Hari, “Performance analysis of Root-Music”,
IEEE Trans. on Acoustics, Speech and Signal Processing, vol. 37, no. 12, pp. 1940-1949, 1989.
|
[19] |
M. L. McCloud and L. L. Scharf, “A new subspace identification algorithm for high-resolution DOA estimation, ”
IEEE Trans. on Antennas&
Propagation, vol. 50, no. 10, pp. 1382-1390, 2002.
|
[20] |
A. L. Swindlehurst, “Maximum likelihood DOA estimation and detection without eigen-decomposition, ” in
IEEE International Conference on Acoustics, vol. V, 1992, pp. 401-404.
|
[21] |
Q. Wu, K. M. Wong, and P. R. James, “Maximum likelihood direction-finding in unknown noise environments, ”
IEEE Trans. on Signal Processing, vol. 42, no. 4, pp. 980-983, 1994.
|
[22] |
I. A. Yuri and A. J. Ben, “Expected likelihood support for deterministic maximum likelihood DOA estimation, ”
Signal Processing, vol. 93, pp. 3410-3422, 2013.
|
[23] |
M. Çetin, D. M. Malioutov, and A. S. Willsky, “A variational technique for source localization based on a sparse signal reconstruction perspective, ” in
2002 IEEE International Conference on Acoustics, Speech, and Signal Processing, Orlando, FL, vol. III, 2002, pp. 2965-2968.
|
[24] |
M. Malioutov, M. Cetin, and A. S. Willsky, “A sparse signal reconstruction perspective for source localization with sensor arrays, ”
IEEE Trans. on Signal Processing, vol. 53, no. 8, pp. 3010-3022, 2005.
|
[25] |
W. Liu, Y. G. Xu, and Z. W. Liu, “DOA estimation of coexisted noncoherent and coherent signals via sparse representation of cleaned array covariance matrix, ”
Journal of Beijing Institute of Technology, vol. 22, no. 2, pp. 241-245, 2013.
|
[26] |
A. Rawat, R. N. Yadav, and S. C. Shrivastava, “Neural network applications in smart antenna arrays: A review, ”
International Journal of Electronics and Communications (AEÜ), vol. 66, pp. 903-912, 2012.
|
[27] |
J. A. Rohwer and Chaouki Abdallah, “One-vs-one multiclass least squares support vector machines for direction of arrival estimation, ”
Applied Computational Electromagnetics Society Journal, vol. 18, pp. 98-109, 2003.
|
[28] |
M. Pastorino and A. Randazzo, “The SVM-based smart antenna for estimation of the directions of arrival of electromagnetic waves, ”
IEEE Transactions on Instrumentation and Measurement, vol. 55, pp. 1918-1925, 2006.
|
[29] |
L. L. Wu, Z. M. Liu, and Z. T. Huang, “Deep convolution network for direction of arrival estimation with sparse prior, ”
IEEE Signal Processing Letters, vol. 26, no. 11, pp. 1688-1692, 2019.
|
[30] |
O. Gültekin, I. Erer, and M. Kaplan, “Empirical mode decomposition based denoising for high resolution direction of arrival estimation, ” in
17th European Signal Processing Conference (EUSIPCO), vol. 1, 2009, pp. 1983-1986.
|
[31] |
X. Wen and W. K. Stewart, “Multiresolution-signal direction-of-arrival estimation: A wavelets approach, ”
IEEE Signal Processing Letters, vol. 7, no. 3, pp. 66-68, 2020.
|
[32] |
G. K. Papageorgiou and M. Sellathurai, “Direction-of-arrival estimation in the low-SNR regime via a denoising autoencoder, ” in
2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), vol. 1, 2020, pp. 1-5.
|
[33] |
W. Guo, T. Qiu, and T. Hong, “Performance of RBF neural networks for array processing in impulsive noise environment, ”
Digital Signal Processing, vol. 18, no. 2, pp. 168-178, 2008.
|
[34] |
S. Lee, Y. J. Yoon, and J. E. Lee, “Two-stage DOA estimation method for low SNR signals in automotive radars, ”
IET Radar Sonar&
Navigation, vol. 11, no. 11, pp. 1613-1619, 2017.
|
[35] |
L. Tian, C. Xu, and D. Chen, “Enhancing mmwave DOA estimation by cumulative power gradient at low SNR, ”
IEEE Signal Processing Letters, vol. 27, pp. 1974-1978, 2020.
|
[36] |
A. B. Gershman, “Direction finding using beamspace root estimator banks, ”
IEEE Trans on Signal Processing, vol. 46, no. 11, pp. 3131-3135, 1998.
|
[37] |
A. B. Gershman, “Pseudo-randomly generated estimator banks: A new tool for improving the threshold performance of direction finding, ”
IEEE Trans on Signal Processing, vol. 46, no. 5, pp. 1351-1364, 1998.
|
[38] |
V. I. Volodymyr, “Direction finding with super-resolution using root implementation of eigenstructure techniques and joint estimation strategy, ” in
European Conference on Wireless Technology 2004, vol. 1, 2004, pp. 101-104.
|
[39] |
V. I. Vasylyshyn, “Beamspace root estimator bank for DOA estimation with an improved threshold performance, ” in
2013 IX Internatioal Conference on Antenna Theory and Techniques, vol. 1, 2013, pp. 280-282.
|
[40] |
B. Ottersten, M. Viberg, and T. Kailath, “Analysis of subspace fitting and ML techniques for parameter estimation from sensor array data, ”
IEEE Trans. Signal Processing, vol. 40, pp. 590-600, 1992.
|
[41] |
Y. Bresler and A. Macovski, “Exact maximum likelihood parameter estimation of superimposed exponential signals in noise, ”
IEEE Trans. Acoust. , Speech, Signal Processing, vol. ASSP-34, pp. 1081-1089, 1986.
|
[42] |
X. Mestre and M. A. Lagunas, “Modified subspace algorithms for DOA estimation with large arrays, ”
IEEE Trans. on Signal Process, vol. 56, no. 2, pp. 598-614, 2008.
|
[43] |
P. Costa, R. N. Carvalho, and K. B. Laskey, “Evaluating uncertainty representation and reasoning in HLF systems, ” in
Information Fusion (FUSION), 2011 Proceedings of the 14th International Conference on, vol. 1, 2011, pp. 1-8.
|
[44] |
J. Liu, Y. Liu, and P. Wei, “Future computer-aided decision-making support systems: Concerning more about the mechanism of human behavior, ”
Journal of Software, vol. 7, no. 3, pp. 712-717, 2012.
|
[45] |
J. Llinas, “Challenges in information fusion technology capabilities for modern intelligence and security problems, ” in
2013 European Intelligence and Security Informatics Conference, vol. 1, 2013, pp. 89-95.
|
[46] |
K. Alexandros and H. Melanie, “Meta-learning, ”
International Journal on Artificial Intelligence Tools, vol. 10, no. 4, pp. 525-554, 2001.
|
[47] |
R. Vilalta and Y. Drissi, “A perspective view and survey of meta-learning, ”
Artificial Intelligence Review, vol. 18, no. 2, pp. 77-95, 2002.
|
[48] |
P. Kordík, J. Černý, and T. Frýda, “Discovering predictive ensembles for transfer learning and meta-learning, ”
Mach Learn, vol. 107, pp. 177-207, 2018.
|
[49] |
E. Barnard, “Optimization for training neural nets, ”
IEEE Trans. Neural Network, vol. 3, no. 1, pp. 232-240, 1992.
|
[50] |
G. E. Hinton, S. Osindero, and Y. W. The, “Reducing the dimensionality of data with neural networks, ”
Science, vol. 313, no. 5786, pp. 1527-1554, 2006.
|
[51] |
C. Cortes and V. Vapnik, “Support vector networks, ”
Machine Learning, vol. 20, pp. 273 - 297, 1995.
|
[52] |
L. Breiman, “Random forests, ”
Machine Learning, vol. 45, no. 1, pp. 5-32, 2001.
|
[53] |
S. Sukhija and C. K. Narayanan, “Supervised heterogeneous feature transfer via random forests, ”
Artificial Intelligence, vol. 268, pp. 30-53, 2019.
|
[54] |
W. Chen and H. R. Pourghasemi, “Landslide spatial modeling: Introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques, ” G
eoderma, vol. 305, pp. 314-327, 2017.
|
[55] |
D. Velusamy and K. Ramasamy, “Ensemble of heterogeneous classifiers for diagnosis and prediction of coronary artery disease with reduced feature subset, ”
Computer Methods and Programs in Biomedicine, vol. 198, pp. 105770, 2021.
|
[56] |
M. Lazri and S. Ameur, “Combination of support vector machine, artificial neural network and random forest for improving the classification of convective and stratiform rain using spectral features of SEVIRI data, ”
Atmospheric Research, vol. 203, pp. 118-129, 2018.
|
[57] |
D. B. Buket, I. Saricicek, and B. Yildirim, “Performance analysis of descriptive statistical features in retinal vessel segmentation via fuzzy logic, ANN, SVM, and classifier fusion, ”
Knowledge-Based Systems, vol. 118, pp. 165-176, 2017.
|
[58] |
C. Ggab, A. Zs, A. Yz, C. Xya, and C. Yhb, “Land use/cover classification in an arid desert-oasis mosaic landscape of China using remote sensed imagery: Performance assessment of four machine learning algorithms, ”
Global Ecology and Conservation, vol. 22, pp. e00971, 2020.
|
[59] |
J. Maroco, D. Silva, A. Rodrigues, M. Guerreiro, I. Santana, and A. Mendon, “Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests, ”
BMC Research Notes, vol. 4, pp. 299, 2011.
|
[60] |
V. B. Andrey, “Neuroinspired architecture for robust classifier fusion of multisensor imagery, ”
IEEE Trans on Geoscience and Remote Sensing, vol. 46, no. 5, pp. 1467-1487, 2008.
|
[61] |
A. J. H. Ma, C. Y. Pong, and J. H. Lai, “Linear dependency modeling for classifier fusion and feature combination, ”
IEEE Trans on Pattern Analysis and Machine Intelligence, vol. 35, no. 5, pp. 1135-1148, 2013.
|
[62] |
K. Shah, H. Patel, D. Sanghvi, and M. Shah, “A comparative analysis of logistic regression, random forest and KNN models for the text classification, ”
Augmented Human Research, vol. 5, pp. 12-27, 2020. DOI:
10.1007/s41133-020-00032-0.
|
[63] |
H. Ye, B. Cui, S. Huang, Y. Dong, and Y. Jin, “Performance of support vector machines, artificial neural network, and random forest for identifying banana fusarium wilt using UAV-based multi-spectral imagery, ” in
Proceedings of the 6th China High Resolution Earth Observation Conference (CHREOC 2019), Lecture Notes in Electrical Engineering, vol. 657, 2019, pp. 261-270.
|
[64] |
S. Lin and Q. Y. Yin, “Searching over DOA parameter space via neural networks, ”
IEEE International Symposium on Circuits&
Systems-ISCAS, vol. 1, pp. 295-298, 1994.
|
[65] |
T. Lo, H. Leung, and J. Litva, “Radial basis function neural network for direction-of-arrivals estimation, ”
IEEE Signal Processing Letters, vol. 1, no. 2, pp. 45-47, 2002.
|
[66] |
W. Guo, T. Qiu, T. Hong, and W. Zhang, “Performance of RBF neural networks for array processing in impulsive noise environment, ”
Digital Signal Processing, vol. 18, no. 2, pp. 168-178, 2008.
|
[67] |
L. Wu, Z. M. Liu, Z. T. Huang, “Deep convolution network for direction of arrival estimation with sparse prior, ”
IEEE Signal Processing Letters, vol. 26, no. 11, pp. 1688-1692, 2019.
|
[68] |
M. Wajid, B. Kumar, A. Goel, A. Kumar, and R. Bahl, “Direction of arrival estimation with uniform linear array based on recurrent neural network, ” in
5th International Conference on Signal Processing, Computing and Control (ISPCC) IEEE, vol. 1, 2019, pp. 361-365.
|
[69] |
A. M. Elbir, “DeepMUSIC: Multiple signal classification via deep learning, ”
IEEE Sensors Letters, vol. 4, no. 4, pp. 1-4, 2020.
|
[70] |
M. Wang, Z. Zhang, and A. Nehorai, “Performance analysis of coarray-based MUSIC in the presence of sensor location errors, ”
IEEE Trans on Signal Processing, vol. 66, pp. 3074-3085, 2018.
|
[71] |
M. Malioutov, M. Cetin, and A. S. Willsky, “A sparse signal reconstruction perspective for source localization with sensor arrays, ”
IEEE Trans. on Signal Processing, vol. 53, no. 8, pp, 3010-3022, 2005.
|
[72] |
Q. Cheng, H. Lei, M. Cao, J. Xie, and H. C. So, “PUMA: An improved realization of MODE for DOA estimation, ”
IEEE Trans. on Aerospace and Electronic Systems, vol. 53, no. 5, pp. 2128-2139, 2017.
|
[73] |
P. Stoica and A. Nehorai, “MUSIC, maximum likelihood and Cramer-Rao bound, ”
IEEE Trans. on Acoust.,
Speech Signal Process, vol. 37, no. 5, pp. 720-741, 1989.
|