Improving the accuracy of a digital spectral correlation-interferometric method of direction finding with analytical signal reconstruction for processing an incomplete spectrum of the signal

Authors

DOI:

https://doi.org/10.15587/1729-4061.2023.288397

Keywords:

error variance, direction finding accuracy, signal spectrum reconstruction, analytical signal reconstruction

Abstract

A method of correlation-interferometric direction finding has been improved, which effectively solves the problem of radio direction finding of radio emission sources under conditions of exposure to one or two masking interference. The problem was solved using the selection of an unmasked fragment of the spatial spectrum of the signal and the reconstruction of the missing samples of its signal group. As a result of the synthesis of the proposed method, estimates of signal samples were obtained as exact solutions to the proposed energy balance equations. The resulting solutions provide a significant increase in the signal-to-interference ratio and, accordingly, direction-finding accuracy without increasing the number of reception channels of the antenna array. As a result of the simulation, the dependences of the standard deviation of the bearing estimate on the signal-to-noise ratio in the presence of interference were built. Under the influence of one or two masking interferences and a signal-to-interference ratio of 0 dB, the use of the known direction-finding method without interference selection produces an anomalously large direction-finding error of more than 0.42 degrees, which is practically independent of the signal-to-noise ratio. The direction-finding method with selection of spectral signal samples masked by interference reduces the direction-finding error to 0.22 degrees when exposed to one interference and to 0.3 degrees when exposed to two interferences. This is due to the presence of power losses of the usable signal during the selection of its samples masked by interference. The proposed method of direction finding with reconstruction of signal samples provides a significant gain in accuracy by 3–30 times compared to the method of selection of masked samples in the range of changes in the signal-to-noise ratio (–20.5) dB. The direction-finding error of the proposed method decreases with increasing signal/noise according to a hyperbolic dependence. It is advisable to use the proposed direction-finding method when masking no more than two samples of the signal group

Author Biographies

Nurzhigit Smailov, Institute of Mechanics and Mechanical Engineering named after Academician U. A. Dzholdasbekov; Satbayev University

PhD

Department Radio Engineering, Electronics and Space Technologies

Vitaliy Tsyporenko, Zhytomyr Polytechnic State University

PhD

Department of Computer Technologies in Medicine and Telecommunications

Akezhan Sabibolda, Satbayev University

Doctoral Student

Department Radio Engineering, Electronics and Space Technologies

Valentyn Tsyporenko, Zhytomyr Polytechnic State University

PhD

Department of Computer Technologies in Medicine and Telecommunications

Assem Kabdoldina, Al-Farabi Kazakh National University; Institute of Mechanics and Mechanical Engineering named after Academician U. A. Dzholdasbekov

PhD

Department Chemical Physics and Materials Science

Maigul Zhekambayeva, Satbayev University

PhD

Department of Software Engineering

Ainur Kuttybayeva, Satbayev University

PhD

Department Radio Engineering, Electronics and Space Technologies

Aldabergen Bektilevov, Institute of Mechanics and Mechanical Engineering named after Academician U. A. Dzholdasbekov; Satbayev University

PhD

Department of Robotics and Technical Tools of Automation

Abdurazak Kassimov, Almaty University of Power Engineering and Telecommunications

PhD

Askar Abdykadyrov, Institute of Mechanics and Mechanical Engineering named after Academician U. A. Dzholdasbekov; Satbayev University

PhD

Department Radio Engineering, Electronics and Space Technologies

References

  1. Rembovskij, A. M. (2015). Radio monitoring – tasks, methods, means. Moscow: Hotline-Telecom, 640.
  2. Sabibolda, A., Tsyporenko, V., Tsyporenko, V., Smailov, N., Zhunussov, K., Abdykadyrov, A. et al. (2022). Improving the accuracy and performance speed of the digital spectral-correlation method for measuring delay in radio signals and direction finding. Eastern-European Journal of Enterprise Technologies, 1 (9 (115)), 6–14. doi: https://doi.org/10.15587/1729-4061.2022.252561
  3. Tsyporenko, V. V., Tsyporenko, V. G., Nikitczuk, T. M. (2019). Optimization of direct digital method of correlative-interferometric direction finding with reconstruction of spatial analytical signal. Radio Electronics, Computer Science, Control, 3, 15–24. doi: https://doi.org/10.15588/1607-3274-2019-3-2
  4. Tsyporenko, V. V., Tsyporenko, V. G., Chukhov, V. V., Andreiev, O. V. (2018). Analysis of Accuracy of Direct Digital Method of Correlative-Interferometric Direction Finding with Two-Dimensional Correlative Processing of Spatial Signal. Visnyk NTUU KPI Seriia - Radiotekhnika Radioaparatobuduvannia, 72, 23–31. doi: https://doi.org/10.20535/radap.2018.72.23-31
  5. Lee, J.-H., Woo, J.-M. (2015). Interferometer Direction-Finding System With Improved DF Accuracy Using Two Different Array Configurations. IEEE Antennas and Wireless Propagation Letters, 14, 719–722. doi: https://doi.org/10.1109/lawp.2014.2377291
  6. Liu, H., Chen, J., Liang, X., Jin, R. (2022). A Compensation Method of Nonideal Modulation Pulse for Direction Finding With Time-Modulated Array. IEEE Antennas and Wireless Propagation Letters, 21 (8), 1577–1581. doi: https://doi.org/10.1109/lawp.2022.3174424
  7. Kornaros, E., Kabiri, S., De Flaviis, F. (2017). A Novel Model for Direction Finding and Phase Center With Practical Considerations. IEEE Transactions on Antennas and Propagation, 65 (10), 5475–5491. doi: https://doi.org/10.1109/tap.2017.2735462
  8. Jiang, X., Ni, G., Cao, A., Shao, C., He, C. (2021). Single-Channel Spatial Spectrum Estimation Direction Finding by the Time-Modulated Linear Array. IEEE Antennas and Wireless Propagation Letters, 20 (12), 2491–2495. doi: https://doi.org/10.1109/lawp.2021.3115826
  9. Liao, B., Wen, J., Huang, L., Guo, C., Chan, S.-C. (2016). Direction Finding With Partly Calibrated Uniform Linear Arrays in Nonuniform Noise. IEEE Sensors Journal, 16 (12), 4882–4890. doi: https://doi.org/10.1109/jsen.2016.2550664
  10. Jiang, Y., Lan, X., Shi, J., Han, Z., Wang, X. (2022). Multi-Target Parameter Estimation of the FMCW-MIMO Radar Based on the Pseudo-Noise Resampling Method. Sensors, 22 (24), 9706. doi: https://doi.org/10.3390/s22249706
  11. Van Brandt, S., Verhaevert, J., Van Hecke, T., Rogier, H. (2022). A New Conformal Map for Polynomial Chaos Applied to Direction-of-Arrival Estimation via UCA Root-MUSIC. Sensors, 22 (14), 5229. doi: https://doi.org/10.3390/s22145229
  12. Lee, J., Jeong, D., Lee, S., Lee, M., Lee, W., Jung, Y. (2023). FPGA Implementation of the Chirp-Scaling Algorithm for Real-Time Synthetic Aperture Radar Imaging. Sensors, 23 (2), 959. doi: https://doi.org/10.3390/s23020959
  13. Ni, G., He, C., Liu, Y., Chen, J., Jin, R. (2020). Direction-Finding Based on Time-Modulated Array Without Sampling Synchronization. IEEE Antennas and Wireless Propagation Letters, 19 (12), 2149–2153. doi: https://doi.org/10.1109/lawp.2020.3025328
  14. Zhang, C., Huang, H., Liao, B. (2017). Direction Finding in MIMO Radar With Unknown Mutual Coupling. IEEE Access, 5, 4439–4447. doi: https://doi.org/10.1109/access.2017.2684465
  15. Xu, Y., Wang, C., Zheng, G., Tan, M. (2023). Nonlinear Frequency Offset Beam Design for FDA-MIMO Radar. Sensors, 23 (3), 1476. doi: https://doi.org/10.3390/s23031476
  16. Tang, T., Jiang, L., Zhao, P., Zheng, N. (2022). Coordinated Positioning Method for Shortwave Anti-Multipath Based on Bayesian Estimation. Sensors, 22 (19), 7379. doi: https://doi.org/10.3390/s22197379
  17. Wang, J., Wang, P., Zhang, R., Wu, W. (2022). SDFnT-Based Parameter Estimation for OFDM Radar Systems with Intercarrier Interference. Sensors, 23 (1), 147. doi: https://doi.org/10.3390/s23010147
  18. Ren, B., Wang, T. (2022). Space-Time Adaptive Processing Based on Modified Sparse Learning via Iterative Minimization for Conformal Array Radar. Sensors, 22 (18), 6917. doi: https://doi.org/10.3390/s22186917
  19. Dai, Y., Liu, D., Hu, Q., Yu, X. (2022). Radar Target Detection Algorithm Using Convolutional Neural Network to Process Graphically Expressed Range Time Series Signals. Sensors, 22 (18), 6868. doi: https://doi.org/10.3390/s22186868
  20. Rosado-Sanz, J., Jarabo-Amores, M. P., De la Mata-Moya, D., Rey-Maestre, N. (2022). Adaptive Beamforming Approaches to Improve Passive Radar Performance in Sea and Wind Farms’ Clutter. Sensors, 22 (18), 6865. doi: https://doi.org/10.3390/s22186865
  21. Li, R., Zhao, L., Liu, C., Bi, M. (2022). Strongest Angle-of-Arrival Estimation for Hybrid Millimeter Wave Architecture with 1-Bit A/D Equipped at Transceivers. Sensors, 22 (9), 3140. doi: https://doi.org/10.3390/s22093140
  22. Xu, K., Deng, Y., Yu, Z. (2022). Distributed Target Detection in Unknown Interference. Sensors, 22 (7), 2430. doi: https://doi.org/10.3390/s22072430
  23. Proakis, J. G. (2006). Digital Signal Processing, Principles, Algorithms, and Applications. New Jersey: Prentice-Hall, Inc. Upper Saddle River, NJ, USA, 1077.
  24. Alessio, S. M. (2016). Digital Signal Processing and Spectral Analysis for Scientists. Springer Cham, 900. doi: https://doi.org/10.1007/978-3-319-25468-5
Improving the accuracy of a digital spectral correlation-interferometric method of direction finding with analytical signal reconstruction for processing an incomplete spectrum of the signal

Downloads

Published

2023-10-31

How to Cite

Smailov, N., Tsyporenko, V., Sabibolda, A., Tsyporenko, V., Kabdoldina, A., Zhekambayeva, M., Kuttybayeva, A., Bektilevov, A., Kassimov, A., & Abdykadyrov, A. (2023). Improving the accuracy of a digital spectral correlation-interferometric method of direction finding with analytical signal reconstruction for processing an incomplete spectrum of the signal. Eastern-European Journal of Enterprise Technologies, 5(9 (125), 14–25. https://doi.org/10.15587/1729-4061.2023.288397

Issue

Section

Information and controlling system