Preview

Journal of the Russian Universities. Radioelectronics

Advanced search

Detection, Parameter Estimation and Direction Finding of Periodic Pulse Signals

https://doi.org/10.32603/1993-8985-2025-28-3-73-84

Abstract

Introduction. Periodic pulse signals are used in various fields, including radar systems. The parameters of periodic pulse signals, such as period, pulse duration and shape, and radio frequency content, can vary significantly and, as a rule, are unknown a priori. Under the conditions of a priori uncertainty and low signal-to-noise ratios, the detection of periodic pulse signals, estimation of their parameters, and direction finding of the source is a non-trivial task.
Aim. To develop algorithms for detecting, estimating parameters, and direction finding of periodic pulse signals in the presence of low signal-to-noise ratios and the absence of a priori information about the parameters of a periodic pulse signal.
Materials and methods. The methods of statistical radio engineering, mathematical statistics, estimation of signal parameters against interference, and computer simulation were used.
Results. Simple-to-implement algorithms for detecting periodic pulse signals, evaluating their parameters, and direction finding of the source have been developed. The noise immunity characteristics of the algorithms obtained by computer simulation were successfully tested when receiving actual signals. The noise immunity of the algorithms was shown to increase with a decrease in the duty ratio of the signal. The developed algorithms allow periodic pulse signals to be distinguished from signals of wireless communication systems, such as GSM, UMTS, LTE, Wi-Fi, 5G. The latter signals, although having a periodic component, are not pulsed signals.
Conclusion. The developed algorithms function successfully in the absence of a priori information about the parameters of a periodic pulse signal and at low signal-to-noise ratios (up to –15 dB). A significant gain in the noise immunity of direction finding was achieved in comparison with the standard phase difference algorithm. The parameters of a periodic pulse signal evaluated using the developed algorithms can be used for source identification.

About the Authors

V. B. Manelis
АО "ИРКОС"
Russian Federation

Vladimir B. Manelis, Dr Sci. (Eng.) (2010), leading researcher

The author of more than 70 scientific publications. Area of expertise: communication systems; radiomonitoring; algorithms for receiving and processing signals.

101b, Rabochiy Ave., Voronezh 394024



I. S. Faustov
АО "ИРКОС"; ФГБОУ ВО "Воронежский государственный технический университет"
Russian Federation

Ivan S. Faustov, engineer in Radio-electronic devices and systems (2021), Postgraduate student of the Department of Radio Engineering; researcher

The author of 11 scientific publications. Area of expertise: radiomonitoring; algorithms for receiving and processing signals; digital signal processing.

101b, Rabochiy Ave., Voronezh 394024



V. A. Kozmin
АО "ИРКОС"
Russian Federation

Vladimir A. Kozmin, Cand. Sci. (Eng) (1989), Associate Professor (1989), Director for scientific work

The author of more than 200 scientific publications. Area of expertise: radiomonitoring; algorithms for receiving and processing signals; digital signal processing.

101b, Rabochiy Ave., Voronezh 394024



References

1. Korotkov V. F., Zyryanov R. S. Pulse Sequence Orsi R. J., Moore J. B., Mahony R. E. Spectrum Division in Mixed Signal Flow. J. of the Russian Universities. Radioelectronics. 2017, no. 3, pp. 5–10. (In Russ.)

2. Mardia H. K. New Techniques for The Deinterleaving of Repetitive Sequences. IEE Proc. F-Radar and Signal Processing. 1989, vol. 136, no. 4, pp. 149–154. doi: 10.1049/ip-f-2.1989.0025

3. Milojevic D. J., Popovic B. M. Improved Algorithm for the Deinterleaving of Radar Pulses. IEE Proc. F-Radar and Signal Processing. 1992, vol. 139, no. 1, pp. 98–104. doi: 10.1049/ip-f-2.1992.0012

4. Ge Z., Sun X., Ren W., Chen W., Xu G. Improved Algorithm of Radar Pulse Repetition Interval Deinterleaving based on Pulse Correlation. IEEE Access. 2019, vol. 7, pp. 30126–30134. doi: 10.1109/ACCESS.2019. 2901013

5. Liu Y., Chen Y., Sun S. A Radar Signal Sorting Algorithm based on PRI. 19th Intern. Symp. on Communications and Information Technologies (ISCIT). Ho Chi Minh City, Vietnam, 25–27 Sept. 2019. IEEE, 2019, pp. 144–149. doi: 10.1109/ISCIT.2019.8905239

6. Zheleznyak V., Barkov A. Detection of Periodic Pulse Sequences and Their Estimation Period. Bull. of the Polotsk State University. Fundamental Sciences. 2012, no. 4, pp. 16–20. (In Russ.)

7. Barkov A., Zheleznyak V. A Method for Suppressing Noisy Pulse Sequences by Compensating. Electronics info. 2013, no. 96, pp. 212–216. (In Russ.)

8. Estimation of Interleaved Pulse Trains. IEEE Trans. on Signal Processing. 1999, vol. 47, iss. 6, pp. 1646–1653. doi: 10.1109/78.765135

9. Ng S. A Technique for Spectral Component Location Within a FFT Resolution Cell. IEEE Intern. Conf. on Acoustics, Speech, and Signal Processing (ICASSP 84), San Diego, USA, 19–21 March 1984. IEEE, 1984, pp. 147–149. doi: 10.1109/icassp.1984.1172774

10. Nelson D. Special Purpose Correlation Functions for Improved Signal Detection and Parameter Estimation. IEEE Intern. Conf. on Acoustics, Speech, and Signal Processing (ICASSP 93), Minneapolis, USA, 27–30 Apr. 1993. IEEE, 1993, pp. 73–76. doi: 10.1109/ICASSP.1993.319597

11. Selim A., Paisana F., Arokkiam J. A., Zhang Y., Doyle L., DaSilva L. A. Spectrum Monitoring for Radar Bands Using Deep Convolutional Neural Networks. IEEE Global Communications Conf. (GLOBECOM 2017), Singapore, 4–8 Dec. 2017. IEEE, 2017, 6 p. doi: 10.1109/GLOCOM.2017.8254105

12. Wang C., Sun L., Liu Z., Huang Z. A Radar Signal Deinterleaving Method Based on Semantic Segmentation with Neural Network. IEEE Trans. on Signal Processing. 2022, vol. 70, pp. 5806–5821. doi: 10.1109/TSP. 2022.3229630

13. Zhu M., Wang S., Li Y. Model-Based Representation and Deinterleaving of Mixed Radar Pulse Sequences With Neural Machine Translation Network. IEEE Trans. on Aerospace and Electronic Systems. 2022, vol. 58, no. 3, pp. 1733–1752. doi: 10.1109/TAES.2021.3122411

14. Han J. W., Park C. H. A Unified Method for Deinterleaving and PRI Modulation Recognition of Radar Pulses Based on Deep Neural Networks. IEEE Access. 2021, vol. 9, pp. 89360–89375. doi: 10.1109/ACCESS. 2021.3091309

15. Wang С., Wang Y., Li X., Ke D. A Deinterleaving Method for Mechanical-Scanning Radar Signals Based on Deep Learning. 7th Intern. Conf. on Intelligent Computing and Signal Processing (ICSP), Xi'an, China, 15–17 Apr. 2022. IEEE, 2022, pp. 138–143. doi: 10.1109/ ICSP54964.2022.9778808

16. Rembovsky А. M., Ashikhmin А. V., Kozmin V. А. Avtomatizirovannyye sistemy radiokontrolya i ikh komponenty [Automated Radio Monitoring Systems and Their Components]. Moscow, Hotline-Telecom, 2017, 424 p. (In Russ.)

17. Liberti J. C., Rappaport T. S. Smart Antennas for Wireless Communication: IS-95 and Third Generation CDMA Applications in IS-95. New Jersey, Prentice Hall, 1999, 376 р.

18. Kulikov E. I., Trifonov A. P. Otsenka parametrov signalov na fone pomekh [Estimation of Signal Parameters in the Presence of Interference]. Moscow, Sov. Radio, 1978, 296 p. (In Russ.)


Review

For citations:


Manelis V.B., Faustov I.S., Kozmin V.A. Detection, Parameter Estimation and Direction Finding of Periodic Pulse Signals. Journal of the Russian Universities. Radioelectronics. 2025;28(3):73-84. (In Russ.) https://doi.org/10.32603/1993-8985-2025-28-3-73-84

Views: 6


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 1993-8985 (Print)
ISSN 2658-4794 (Online)