Mobile Networks and Applications

, Volume 23, Issue 4, pp 743–751 | Cite as

DOA Estimation to Mixed Signals in the Presence of Gain-Phase Perturbation

  • Jiaqi ZhenEmail author


The traditional direction of arrival (DOA) estimation method is very sensitive to the array perturbation, but there are often various errors or perturbations in application, which directly influence the estimation seriously. Therefore, we address the problem of direction of arrival (DOA) estimation to mixed far-filed and near-field signals in the presence of gain-phase perturbation, which can effectively calculate the DOA of far-field signal (FS) and location of near-field signal (NS). First, the spatial spectrum of FS is simplified according to the structure of the array, thus, the DOA can be obtained by finding the roots of the corresponding determinant. Second, gain-phase perturbation is determined based on the orthogonality between noise subspace and signal subspace of FS. Finally, DOA of NS is skillfully acquired through matrix transformation, and then their location can also be decided in the open space. Simulation results demonstrate the effectiveness of the proposed method.


Direction of arrival Gain-phase perturbation Far-field signal Near-field signal 



This work was supported by the National Natural Science Foundation of China (61501176) and the University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province (UNPYSCT-2016017).


  1. 1.
    Lin Y, Wang C, Ma C et al (2016) A new combination method for multisensor conflict information. J Supercomput 72:2874–2890CrossRefGoogle Scholar
  2. 2.
    Lin Y, Wang C, Wang JX et al (2016) A novel dynamic spectrum access framework based on reinforcement learning for cognitive radio sensor networks. Sensors 16:1–22CrossRefGoogle Scholar
  3. 3.
    Liu S, Fu W, He L et al (2017) Distribution of primary additional errors in fractal encoding method. Multimedia Tools Appl 76:5787–5802CrossRefGoogle Scholar
  4. 4.
    Liu S, Pan Z, Fu W et al (2017) Fractal generation method based on asymptote family of generalized Mandelbrot set and its application. J Nonlinear Sci Appl 10:1148–1161MathSciNetCrossRefGoogle Scholar
  5. 5.
    Daegun O, Ju YH, Nam H et al (2016) Dual smoothing DOA estimation of two-channel FMCW radar. IEEE Trans Aerosp Electron Syst 52:904–917CrossRefGoogle Scholar
  6. 6.
    Li JF, Jiang DF, Zhang XF (2017) DOA estimation based on combined unitary ESPRIT for coprime MIMO radar. IEEE Commun Lett 21:96–99CrossRefGoogle Scholar
  7. 7.
    Ding GR, Wang JL, Wu QH et al (2016) Cellular-base-station assisted device-to-device communications in TV white space. IEEE J Sel Areas Commun 34:107–121CrossRefGoogle Scholar
  8. 8.
    Wang GD, Zhao YX, Huang J et al (2017) The Controller Placement Problem in Software Defined Networking: A Survey. IEEE Netw Mag 31:21–27CrossRefGoogle Scholar
  9. 9.
    Wu QD, Li YB, Lin Y et al (2014) The nonlocal sparse reconstruction algorithm by similarity measurement with shearlet feature vector. Math Probl Eng 2014:1–8MathSciNetGoogle Scholar
  10. 10.
    Wu QD, Li YB, Lin Y et al (2017) The application of nonlocal total variation in image denoising for mobile transmission. Multimedia Tools Appl 2017:17179–17191CrossRefGoogle Scholar
  11. 11.
    David A, Hector A, Sanchez H et al (2016) Indoor Blind Localization of Smartphones by Means of Sensor Data Fusion. IEEE Trans Instrum Meas 65:783–794CrossRefGoogle Scholar
  12. 12.
    Zhang RN, Wang SC, Lu XF et al (2016) Two-Dimensional DOA Estimation for Multipath Propagation Characterization Using the Array Response of PN-Sequences. IEEE Trans Wirel Commun 15:341–356CrossRefGoogle Scholar
  13. 13.
    Mohamedatni Y, Fergani B, Laheurte B et al (2015) DOA estimation techniques applied to RFID tags using receiving uniform linear array. In: 2015 I.E. International Symposium on Antenna and Propagation and USNC/URSI National Radio Science Meeting. International Union of Radio Science, Vancouver, p 1760–1761Google Scholar
  14. 14.
    Han G, Wan L, Shu L et al (2015) Two Novel DOA Estimation Approaches for Real-Time Assistant Calibration Systems in Future Vehicle Industrial. IEEE Sys J 99:1–12Google Scholar
  15. 15.
    Wang XR, Amin M, Ahmad F et al (2017) Elias Aboutanios, Interference DOA estimation and suppression for GNSS receivers using fully augmentable arrays. IET Radar Sonar Navig 11:474–480CrossRefGoogle Scholar
  16. 16.
    Jean O, Weiss AJ (2014) Geolocation by direction of arrival using arrays with unknown orientation. IEEE Trans Signal Process 62:3135–3142MathSciNetCrossRefGoogle Scholar
  17. 17.
    Wang WY, Du QR, Wu RB (2015) Interference suppression with flat gain constraint for satellite navigation systems. IET Radar Sonar Navig 9:852–856CrossRefGoogle Scholar
  18. 18.
    Chen X, Morton Y (2016) Iterative subspace alternating projection method for GNSS multipath DOA estimation. IET Radar Sonar Navig 10:1260–1269CrossRefGoogle Scholar
  19. 19.
    Schmidt RO (1986) Multiple emitter location and signal parameter estimation. IEEE Trans Antennas Propag 34:276–280CrossRefGoogle Scholar
  20. 20.
    Roy R, Kailath T (1989) ESPRIT-estimation of signal parameters via rotational invariance techniques. IEEE Trans Acoust Speech Signal Process 37:984–995CrossRefzbMATHGoogle Scholar
  21. 21.
    Ziskind I, Wax M (1988) Maximum likelihood localization of multiple sources by alternating projection. IEEE Trans Acoust Speech Signal Process 36:1553–1559CrossRefzbMATHGoogle Scholar
  22. 22.
    Heidenreich P, Zoubir AM, Rubsamen M (2012) Joint 2-D DOA estimation and phase calibration for uniform rectangular arrays. IEEE Trans Signal Process 60:4683–4693MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Liu AF, Liao GS, Zeng C (2011) An Eigenstructure Method for Estimating DOA and Sensor Gain-Phase Errors. IEEE Trans Signal Process 59:5944–5956MathSciNetCrossRefzbMATHGoogle Scholar
  24. 24.
    Mavrychev EA, Ermolayev VT, Flaksman AG (2013) Robust Capon-based direction-of-arrival estimators in partly calibrated sensor array. Signal Process 93:3459–3465CrossRefGoogle Scholar
  25. 25.
    Cao SH, Ye ZF, Hu N (2013) DOA estimation based on fourth-order cumulants in the presence of sensor gain-phase errors. Signal Process 93:2581–2585CrossRefGoogle Scholar
  26. 26.
    Cao SH, Ye ZF, Xu DY (2013) A Hadamard Product Based Method for DOA Estimation and Gain-Phase Error Calibration. IEEE Trans Aerosp Electron Syst 49:1224–1233CrossRefGoogle Scholar
  27. 27.
    He K, Wang WH, Cui XM (2014) A calibration method for position, gain and phase uncertainty of nonplanar array with arbitrary geometry. In: 2014 I.E. International Conference on Signal Processing, Communications and Computing. ICSPCC, Guilin, China, pp 587–590Google Scholar
  28. 28.
    Han KY, Yang P, Nehorai A (2015) Calibrating nested sensor arrays with model errors. IEEE Trans Antennas Propag 63:4739–4748MathSciNetCrossRefGoogle Scholar
  29. 29.
    He J, Swamy MNS, Ahmad MO (2012) Efficient application of MUSIC algorithm under the coexistence of far-field and near-field sources. IEEE Trans Signal Process 60:2066–2070MathSciNetCrossRefzbMATHGoogle Scholar
  30. 30.
    Wang K, Wang L, Shang JR (2016) Mixed Near-Field and Far-Field Source Localization Based on Uniform Linear Array Partition. IEEE Sensors J 16:8083–8090Google Scholar
  31. 31.
    Jiang JJ, Duan FJ, Chen J (2013) Mixed near- field and far-field sources localization using the uniform linear sensor array. IEEE Sensors J 13:3136–3143CrossRefGoogle Scholar
  32. 32.
    Liu GH, Sun XY (2014) Spatial Differencing Method for Mixed Far-Field and Near-Field Sources Localization. IEEE Signal Process Lett 21:1331–1335CrossRefGoogle Scholar
  33. 33.
    Liu GH, Sun XY (2014) Two-stage matrix differencing algorithm for mixed far-field and near-field sources classification and localization. IEEE Sensors J 14:1957–1965CrossRefGoogle Scholar
  34. 34.
    Ye T, Sun XY (2014) Mixed sources localisation using a sparse representation of cumulant vectors. IET Signal Process 8:606–611CrossRefGoogle Scholar
  35. 35.
    Vleck EBV (1914) The Influence of Fourier’s Serious upon the Development of Mathematics. Science 39:113–124CrossRefGoogle Scholar
  36. 36.
    Xie J, Tao HH, Rao X et al (2016) Localization of mixed far-field and near-field sources under unknown mutual coupling. Digital Signal Process 50:229–239CrossRefGoogle Scholar
  37. 37.
    Prasad S, Williams RT, Mahalanabis AK (1988) A transform-based covariance differencing approach for some classes of parameter estimation problems. IEEE Trans Acoust Speech Signal Process 36:631–641CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Electronic EngineeringHeilongjiang UniversityHarbinChina

Personalised recommendations