Volume 11 Issue 3
Jun.  2022
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Article Navigation >  Journal of Radars  > 2022  >  11(3): 434-442
YE Hongzhen, GUO Haizhao, GUAN Haoliang, et al. Multi-emitters direct localization method via multi-dictionaries and hierarchical block sparse Bayesian framework[J]. Journal of Radars, 2022, 11(3): 434–442. doi: 10.12000/JR21162
Citation: YE Hongzhen, GUO Haizhao, GUAN Haoliang, et al. Multi-emitters direct localization method via multi-dictionaries and hierarchical block sparse Bayesian framework[J]. Journal of Radars, 2022, 11(3): 434–442. doi: 10.12000/JR21162
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Multi-emitters Direct Localization Method via Multi-dictionaries and Hierarchical Block Sparse Bayesian Framework

DOI: 10.12000/JR21162
  • 1.

    Research Institute of Electronic Science and Technology, University of Science and Technology of China, Chengdu 611731, China

  • 2.

    54th Research Institute of CETC, Shijiazhuang 050081, China

  • 3.

    School of Information and Communication Engineering, University of Science and Technology of China, Chengdu 611731, China

Funds:  The National Natural Science Foundation of China (62171092)
More Information
  • Corresponding author: ZHANG Shunsheng, zhangss@uestc.edu.cn
  • Received Date: 2021-10-31
  • Accepted Date: 2021-12-17
  • Rev Recd Date: 2021-12-16
    • Available Online: 2021-12-21
  • Publish Date: 2022-01-17
    Abstract
  • The direct position determination method based on compressed sensing depends on the accurate signal propagation model. With partially unknown propagation model parameters, its location performance will decline significantly. Thus, this study proposed a localization method via multi-dictionaries and hierarchical block sparse Bayesian framework. Herein, the emitter location problem is transformed into recovering signals from different dictionaries but with shared sparsity, and the emitter location with channel attenuation is solved by a multi-dictionary combination. Simulation results revealed that the algorithm has better performance than the traditional Sparse Bayesian Learning (SBL) method and Direct Position Determination (DPD) method under the condition of low signal-to-noise ratio and a few snapshots.

     

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    • References(21)
  • [1]
    郭英, 冯茗杨, 孙玉曦, 等. 一种基于蓝牙室内指纹定位的贝叶斯改进算法[J]. 测绘通报, 2019(5): 1–6. doi: 10.13474/j.cnki.11-2246.2019.0138

    GUO Ying, FENG Mingyang, SUN Yuxi, et al. An improved algorithm of Bayesian fingerprint localization based on Bluetooth[J]. Bulletin of Surveying and Mapping, 2019(5): 1–6. doi: 10.13474/j.cnki.11-2246.2019.0138
    [2]
    SUN Sibo, ZHANG Xinyu, ZHENG Ce, et al. Underwater acoustical localization of the black box utilizing single autonomous underwater vehicle based on the second-order time difference of arrival[J]. IEEE Journal of Oceanic Engineering, 2020, 45(4): 1268–1279. doi: 10.1109/JOE.2019.2950954
    [3]
    SUN Sibo, QIN Shasha, HAO Yu, et al. Underwater acoustic localization of the black box based on generalized second-order time difference of arrival (GSTDOA)[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(9): 7245–7255. doi: 10.1109/TGRS.2020.3032982
    [4]
    WEISS A J. Direct position determination of narrowband radio frequency transmitters[J]. IEEE Signal Processing Letters, 2004, 11(5): 513–516. doi: 10.1109/LSP.2004.826501
    [5]
    MALIOUTOV D, CETIN M, and WILLSKY A S. A sparse signal reconstruction perspective for source localization with sensor arrays[J]. IEEE Transactions on Signal Processing, 2005, 53(8): 3010–3022. doi: 10.1109/TSP.2005.850882
    [6]
    TIBSHIRANI R. Regression shrinkage and selection via the lasso: A retrospective[J]. Journal of the Royal Statistical Society:Series B (Statistical Methodology) , 2011, 73(3): 273–282. doi: 10.1111/j.1467-9868.2011.00771.x
    [7]
    TIPPING M E. Sparse Bayesian learning and the relevance vector machine[J]. The Journal of Machine Learning Research, 2001, 1(3): 211–244. doi: 10.1162/15324430152748236
    [8]
    WIPF D P and RAO B D. Sparse Bayesian learning for basis selection[J]. IEEE Transactions on Signal Processing, 2004, 52(8): 2153–2164. doi: 10.1109/TSP.2004.831016
    [9]
    ZHANG Zhilin and RAO B D. Sparse signal recovery with temporally correlated source vectors using sparse Bayesian learning[J]. IEEE Journal of Selected Topics in Signal Processing, 2011, 5(5): 912–926. doi: 10.1109/JSTSP.2011.2159773
    [10]
    ZHANG Zhilin and RAO B D. Extension of SBL algorithms for the recovery of block sparse signals with intra-block correlation[J]. IEEE Transactions on Signal Processing, 2013, 61(8): 2009–2015. doi: 10.1109/TSP.2013.2241055
    [11]
    BABACAN S D, MOLINA R, and KATSAGGELOS A K. Bayesian compressive sensing using Laplace priors[J]. IEEE Transactions on Image Processing, 2010, 19(1): 53–63. doi: 10.1109/TIP.2009.2032894
    [12]
    NANNURU S, GEMBA K L, GERSTOFT P, et al. Sparse Bayesian learning with multiple dictionaries[J]. Signal Processing, 2019, 159: 159–170. doi: 10.1016/j.sigpro.2019.02.003
    [13]
    SUN Baoming, GUO Yan, LI Ning, et al. Multiple target counting and localization using variational Bayesian EM algorithm in wireless sensor networks[J]. IEEE Transactions on Communications, 2017, 65(7): 2985–2998. doi: 10.1109/TCOMM.2017.2695198
    [14]
    游康勇, 杨立山, 刘玥良, 等. 基于稀疏贝叶斯学习的网格自适应多源定位[J]. 电子与信息学报, 2018, 40(9): 2150–2157. doi: 10.11999/JEIT171238

    YOU Kangyong, YANG Lishan, LIU Yueliang, et al. Adaptive grid multiple sources localization based on sparse Bayesian learning[J]. Journal of Electronics &Information Technology, 2018, 40(9): 2150–2157. doi: 10.11999/JEIT171238
    [15]
    CHEN Minqiu, MAO Xingpeng, and ZHAO Chunlei. Direct localization of emitters based on sparse Bayesian learning[J]. IEEE Transactions on Vehicular Technology, 2019, 68(6): 5769–5781. doi: 10.1109/TVT.2019.2910831
    [16]
    YOU Kangyong, GUO Wenbin, PENG Tao, et al. Parametric sparse Bayesian dictionary learning for multiple sources localization with propagation parameters uncertainty[J]. IEEE Transactions on Signal Processing, 2020, 68: 4194–4209. doi: 10.1109/TSP.2020.3009875
    [17]
    SCHMITZ J, MATHAR R, and DORSCH D. Compressed time difference of arrival based emitter localization[C]. The 3rd International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing (CoSeRa), Pisa, Italy, 2015. doi: 10.1109/CoSeRa.2015.7330305.
    [18]
    HINCAPIE R, GOMEZ C, BETANCUR L, et al. Sparse framework for hybrid TDoA/DoA multiple emitter localization[C]. 2017 IEEE International Symposium on Signal Processing and Information Technology, Bilbao, Spain, 2017. doi: 10.1109/ISSPIT.2017.8388637.
    [19]
    CARLIN M, ROCCA P, OLIVERI G, et al. Directions-of-arrival estimation through Bayesian compressive sensing strategies[J]. IEEE Transactions on Antennas and Propagation, 2013, 61(7): 3828–3838. doi: 10.1109/TAP.2013.2256093
    [20]
    张政超, 童力. 四站时差无源定位精度分析[J]. 中国电子科学研究院学报, 2010, 5(6): 582–585. doi: 10.3969/j.issn.1673-5692.2010.06.007

    ZHANG Zhengchao and TONG Li. Precision analysis of passive location of 4-stations based on TDOA[J]. Journal of China Acadewy of Electronics and Information Technology, 2010, 5(6): 582–585. doi: 10.3969/j.issn.1673-5692.2010.06.007
    [21]
    TIRER T and WEISS A J. High resolution direct position determination of radio frequency sources[J]. IEEE Signal Processing Letters, 2016, 23(2): 192–196. doi: 10.1109/LSP.2015.2503921
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