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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
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
shu

Effective DOA Estimation Under Low Signal-to-Noise Ratio Based on Multi-Source Information Meta Fusion

doi:10.15918/j.jbit1004-0579.2021.052
  • 1.

    Acoustic Science and Technology Laboratory and the College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, China

  • 2.

    Key Laboratory of Marine Information Acquisition and Security (Harbin Engineering University), Ministry of Industry and Information Technique, Harbin 150001, China

  • 3.

    School of Physics and Electronic Engineering, Northeast Petroleum University, Daqing 163318, Heilongjiang, China

Funds:This work was supported by the National Natural Science Foundation of China(Nos. 11774073 and 51279033).
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  • Author Bio:

    Yun Wuwas born in Daqing, China in 1980. She received the B.S. and M.S. degrees in electronic engineering from the Northeast Petroleum University, Daqing, in 2002 and 2008 respectively. She is currently working toward the Ph.D. degree of information and communication engineering in the College of Underwater Acoustic Engineering, Harbin Engineering University. She is also an associate professor at the Northeast Petroleum University, Daqing, China. Her research interests are underwater signal processing, array processing, and big data based pattern recognition

    Xiukun Lireceived the B.S. in electronic engineering from Harbin Ship Engineering Institute, in 1984, the M.S. and Ph.D. degrees in underwater acoustic engineering from Harbin Engineering University (HEU) in 1989 and in 2000, respectively. Since 2001, she has been a professor and doctoral supervisor at HEU. Her research interests include underwater signal processing, array processing, and target detection, and etc. She has published more than 60 peer-reviewed papers, and she is also the inventor or coinventor of more than 10 patents

    Zhimin Caoreceived the B.S. degree in electronic engineering from Daqing Petroleum Institute, Daqing, China, in 2003, the M.S. and Ph.D. degrees in information and communication engineering from Harbin Institute of Technology, Harbin, China, in 2008 and in 2016 respectively. In 2020, he has finished his postdoctoral work in Daqing OilFiled Postdoctoral Workstation. He is now an associate professor in the School of Physical and Electronic Engineering, Northeast Petroleum University. His research interests are signal processing and pattern recognition, geological Big Data analysis and application. He has published more than 40 peer-review papers

  • Corresponding author:Email:lixiukun@hrbeu.edu.cn
  • Received Date:2021-07-22
  • Rev Recd Date:2021-08-22
  • Accepted Date:2021-09-12
  • Publish Date:2021-12-27
    Abstract
  • Efficiently performing high-resolution direction of arrival (DOA) estimation under low signal-to-noise ratio (SNR) conditions has always been a challenge task in the literatures. Obviously, in order to address this problem, the key is how to mine or reveal as much DOA related information as possible from the degraded array outputs. However, it is certain that there is no perfect solution for low SNR DOA estimation designed in the way of winner-takes-all. Therefore, this paper proposes to explore in depth the complementary DOA related information that exists in spatial spectrums acquired by different basic DOA estimators. Specifically, these basic spatial spectrums are employed as the input of multi-source information fusion model. And the multi-source information fusion model is composed of three heterogeneous meta learning machines, namely neural networks (NN), support vector machine (SVM), and random forests (RF). The final meta-spectrum can be obtained by performing a final decision-making method. Experimental results illustrate that the proposed information fusion based DOA estimation method can really make full use of the complementary information in the spatial spectrums obtained by different basic DOA estimators. Even under low SNR conditions, promising DOA estimation performance can be achieved.
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  • [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.
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