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Signal separation and super-resolution DOA estimation based on multi-objective joint learning

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Abstract

The performance of traditional physics-driven direction-of-arrival (DOA) methods suffers from array imperfection, coherent signals, and computational complexity. In this paper, the multi-objective joint learning (MOJL) model is proposed to mine the latent joint features of multiple coherent signals and separate them into different subnetworks. The data of each signal are reconstructed and used for super-resolution DOA estimation according to the output of the subnetwork. The simulation results show that the proposed method can separate the data of multiple coherent signals with small errors. The statistical results demonstrate that the performance of the proposed method is superior to the traditional physics-driven methods and state-of-the-art data-driven methods in DOA estimation accuracy, and generalization to signal-to-noise ratio (SNR) and array imperfection.

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References

  1. Tomic S, Beko M, Dinis R (2017) 3-D Target localization in wireless sensor networks using RSS and AoA measurements. IEEE Trans Veh Technol 66(4):3197–3210

    Article  Google Scholar 

  2. Wan L, Han G, Shu L, Chan S, Zhu T (2016) The application of DOA estimation approach in patient tracking systems with high patient density. IEEE Trans Industr Inform 12(6):2353–2364

    Article  Google Scholar 

  3. Xu J, Wang W-Q, Gui R (2019) Computational efficient DOA, DOD, and Doppler estimation algorithm for MIMO radar. IEEE Signal Processing Letters 26(1):44–48

    Article  Google Scholar 

  4. Huang H, Yang J, Huang H, Song Y, Gui G (2018) Deep learning for super-resolution channel estimation and DOA estimation based massive MIMO system. IEEE Trans Veh Technol 67(9):8549–8560

    Article  Google Scholar 

  5. Yang T, Zheng J, Su T, Liu H (2021) Fast and robust super-resolution DOA estimation for UAV swarms. Signal Processing 188:108187

    Article  Google Scholar 

  6. Schmidt R (1986) Multiple emitter location and signal parameter estimation. IEEE Trans Antennas Propag 34(3):276–280

    Article  Google Scholar 

  7. Roy R, Kailath T (1989) ESPRIT-Estimation of signal parameters via rotational invariance techniques. IEEE Trans Acoust Speech Signal Process 37(7):984–995

    Article  MATH  Google Scholar 

  8. Ziskind I, Wax M (1988) Maximum likelihood localization of multiple sources by alternating projection. IEEE Trans Acoust Speech Signal Process 36(10):1553–1560

    Article  MATH  Google Scholar 

  9. Wu X, Zhu W -P, Yan J (2016) Direction of arrival estimation for off-grid signals based on sparse Bayesian learning. IEEE Sensors J 16(7):2004–2016

    Article  Google Scholar 

  10. Sedighi S, Mysore BS, Soltanalian RM, Ottersten B (2021) On the performance of one-Bit DoA estimation via sparse linear arrays. IEEE Trans Signal Process 69:6165–6182

    Article  MathSciNet  Google Scholar 

  11. Xiang H, Chen B, Yang M, Li C (2019) Altitude measurement based on characteristics reversal by deep neural network for VHF radar, IET Radar. Sonar & Navigation 13(1):98–103. [Online]. Available: https://doi.org/10.1049/iet-rsn.2018.5121

    Article  Google Scholar 

  12. Wu L-L, Huang Z-T (2019) Coherent SVR learning for wideband direction-of-arrival estimation. IEEE Signal Processing Letters 26(4):642–646

    Article  Google Scholar 

  13. Liu Z-M, Zhang C, Yu PS (2018) Direction-of-arrival estimation based on deep neural networks with robustness to array imperfections. IEEE Trans Antennas Propag 66(12):7315–7327

    Article  Google Scholar 

  14. Xiang H, Chen B, Yang M, Yang T, Liu D (2019) A novel phase enhancement method for low-angle estimation based on supervised dnn learning. IEEE Access 7:82329–82336

    Article  Google Scholar 

  15. Xiang H, Chen B, Yang M, Xu s., Li Z (2021) Improved direction-of-arrival estimation method based on LSTM neural networks with robustness to array imperfections. Appl Intell 51(7):4420–4433

    Article  Google Scholar 

  16. Yang Y, Gao F, Qian C, Liao G (2020) Model-aided deep neural network for source number detection. IEEE Signal Processing Letters 27:91–95

    Article  Google Scholar 

  17. Huang H, Yang J, Huang H, Song Y, Gui G (2018) Deep learning for super-resolution channel estimation and DOA estimation based massive MIMO system. IEEE Trans Veh Technol 67(9):8549–8560

    Article  Google Scholar 

  18. Elbir AM (2020) deepMUSIC: Multiple signal classification via deep learning. IEEE Sensors Letters 4(4):1–4

    Article  Google Scholar 

  19. Xiang H, Chen B, Yang T, Liu D (2020) Phase enhancement model based on supervised convolutional neural network for coherent doa estimation. Appl Intell 50:2411–2422

    Article  Google Scholar 

  20. Xiang H, Chen B, Xu S, Li Z (2021) Improved direction-of-arrival estimation method based on lstm neural networks with robustness to array imperfections. Appl Intell 51:4420–4433

    Article  Google Scholar 

  21. Wang J, Jiang C, Zhang H, Ren Y, Chen KC, Hanzo L (2019) Thirty years of machine learning: The road to pareto-optimal wireless networks. IEEE Communications Surveys and Tutorials

  22. Akter R, Doan V-S, Huynh-The T, Kim D-S (2021) RFDOA-Net: An efficient convNet for RF-based DOA estimation in UAV surveillance systems. IEEE Trans Veh Technol 70(11):12209–12214

    Article  Google Scholar 

  23. Hu C, He S, Wang Y (2021) A classification method to detect faults in a rotating machinery based on kernelled support tensor machine and multilinear principal component analysis. Appl Intell 51:2609–2621

    Article  Google Scholar 

  24. Hu C, Wang Y, Gu J (2020) Cross-domain intelligent fault classification of bearings based on tensor-aligned invariant subspace learning and two-dimensional convolutional neural networks. Knowledge-Based Systems, (209): 106214

  25. Wan L, Sun Y, Sun L, Ning Z, Rodrigues JJPC (2021) Deep learning based autonomous vehicle super resolution DOA estimation for safety driving. IEEE Trans Intell Trans Syst 22(7):4301–4315

    Article  Google Scholar 

  26. Agatonović M, Stanković Z, Milovanović B, Donč ov N (2011) DOA estimation using radial basis function neural networks as uniform circular antenna array signal processor. In: 2011 10th international conference on telecommunication in modern satellite cable and broadcasting services (TELSIKS), vol 2, pp 544–547

  27. Ziskind I, Wax M (1988) Maximum likelihood localization of multiple sources by alternating projection. IEEE Trans Acoust Speech Signal Process 36(10):1553–1560

    Article  MATH  Google Scholar 

  28. Shan T, Wax M, Kailath T (1985) On spatial smoothing for direction-of-arrival estimation of coherent signals. IEEE Trans Acoust Speech Signal Process 33(4):806–811

    Article  Google Scholar 

  29. Randazzo A, Abou-Khousa MA, Pastorino M, Zoughi R (2007) Direction of arrival estimation based on support vector regression: experimental validation and comparison with MUSIC. IEEE Antennas Wirel Propag Lett 6:379–382

    Article  Google Scholar 

  30. El Zooghby A, Christodoulou C, Georgiopoulos M (2000) A neural network-based smart antenna for multiple source tracking. IEEE Trans Antennas Propag 48(5):768–776

    Article  Google Scholar 

  31. Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: Proc of the 3rd international conference for learning representations (ICLR)

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Acknowledgements

This work was supported in part by the fundamental Research Funds for the Central University, and the National Natural Science Foundation of China under Grant on 61971323 and 61771180. The authors sincerely express their gratitude to anonymous reviewers and editor for their helpful and constructive comments and suggestions.

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Authors and Affiliations

  1. Key Laboratory of Knowledge Engineering with Big Data, Ministry of Education, Hefei University of Technology, Hefei, 230601, China

    Houhong Xiang, Meibin Qi & Zhuang Shuo

  2. Intelligent Interconnected Systems Laboratory of Anhui Province, Hefei University of Technology, Hefei, 230601, China

    Houhong Xiang, Meibin Qi & Zhuang Shuo

  3. National Laboratory of Radar Signal Processing, Xidian University, Xi’an, 710071, China

    Baixiao Chen

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  1. Houhong Xiang
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  2. Meibin Qi
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Correspondence to Houhong Xiang.

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Xiang, H., Qi, M., Chen, B. et al. Signal separation and super-resolution DOA estimation based on multi-objective joint learning. Appl Intell 53, 14565–14578 (2023). https://doi.org/10.1007/s10489-022-03694-y

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