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
DOI:
https://doi.org/10.15587/1729-4061.2023.288397Keywords:
error variance, direction finding accuracy, signal spectrum reconstruction, analytical signal reconstructionAbstract
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
References
- Rembovskij, A. M. (2015). Radio monitoring – tasks, methods, means. Moscow: Hotline-Telecom, 640.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Xu, K., Deng, Y., Yu, Z. (2022). Distributed Target Detection in Unknown Interference. Sensors, 22 (7), 2430. doi: https://doi.org/10.3390/s22072430
- Proakis, J. G. (2006). Digital Signal Processing, Principles, Algorithms, and Applications. New Jersey: Prentice-Hall, Inc. Upper Saddle River, NJ, USA, 1077.
- 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
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Nurzhigit Smailov, Vitaliy Tsyporenko, Akezhan Sabibolda, Valentyn Tsyporenko, Assem Kabdoldina, Maigul Zhekambayeva, Ainur Kuttybayeva, Aldabergen Bektilevov, Abdurazak Kassimov, Askar Abdykadyrov
This work is licensed under a Creative Commons Attribution 4.0 International License.
The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.
A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.