V.Yu. Semenov1
1 National Research Lobachevsky State University of Nizhny Novgorod (Nizhny Novgorod, Russia)
1 vitali.semenov@gmail.com
The article is intended for specialists in the field of active and passive location, as well as electronic warfare. It considers two methods for solving classical radar problems – suppression of input interference and reception of a useful signal reflected from a target when exposed to interference. A rather complex case is investigated, when the useful signal and active noise interference are broadband processes, and the interference is pulsed. To take into account the broadband nature of the interference, a multitap delay line is built into each receiving channel. The signals in the delay line taps are multiplied by weighting coefficients and summed up, forming the output signal of the antenna array. The set of weighting coefficients is combined into a weight vector, the dimension of which is equal to the product of the number of receiving channels of the array and the number of taps of the delay lines. The paper proposes methods for forming a weight vector based on its representation in the form of a finite expansion in the basis of power vectors. Power vectors are formed by cyclic action of the correlation matrix of the input process in the antenna array on the vector-phasor of the useful signal. They have the physical meaning of correlation vectors, which allows using correlation devices for their formation. The paper presents rigorous analytical derivations of expressions for weight coefficients (weight vectors). Unlike known works, the main attention is paid to the case of a short sample of the input process in the receiving channels of the array, when the number of sample vectors is less than the dimension of the weight vector. In this case, the maximum likelihood estimate of the correlation matrix is singular and it is necessary to apply regularized methods for estimating the weight vector. In the article, regularized solutions for weight vectors are obtained, based on a statistically correct limitation of the dimension of the power basis. The physical meaning of regularization is to estimate the effective number of active impulse interference, based on different criteria. The main advantage of the proposed spatiotemporal signal processing is the ability to adaptively estimate the number of active interference, the frequency band they occupy, and suppress interference using a minimum number of samples per interference. It is assumed that a time interval is allocated during the probing period to calculate the interference correlation matrix.
The computational complexity of the proposed methods is estimated and it is shown that they involve significantly less computation than the traditional method of direct inversion of the maximum likelihood estimate of the input process correlation matrix. A practical approach is proposed for implementing the power method in software due to the iterative structure of the weight vector calculation algorithm itself.
The results of numerical modeling of the proposed method efficiency are presented. An M-sequence was used as a useful signal, and a pulsed broadband interference was formed as a sum of closely spaced tonal interference in a certain frequency band with random amplitudes and phases and with specified probability density functions. The pulse nature of the interference was realized using the threshold technique. The presented results demonstrate the high efficiency of the proposed methods of spatiotemporal signal processing.
Semenov V.Yu. Methods of spatial-temporal signal processing for suppression of pulse interference in wideband adaptive antenna arrays. Electromagnetic waves and electronic systems. 2025. V. 30. № 4. P. 52−66. DOI: https://doi.org/10.18127/j15604128-202504-05 (in Russian)
- Handbook of radar. Ed. by M.I. Skolnik. Transl. from English. Under the general ed. by V.S. Verba. In 2 books. Book 1. Moscow: Technosphere. 2014. 672 p. (in Russian)
- Radio electronic systems. Fundamentals of construction and theory: a reference book. Ed. by Ya.D. Shirman. Moscow: Radio Engineering. 2007. 512 p. (in Russian)
- Stoica P., Moses R.L. Spectral analysis of signal. NJ: Prentice Hall. 2005. 447 p.
- Monzingo R.A., Miller T.U. Adaptive antenna arrays: Introduction to theory. Moscow: Radio and Communications. 1986. 448 p. (in Russian)
- El-Khamy S., El-Sayed H., Eltrass A. A New adaptive beamforming of multiband fractal antenna array in strong-jamming environment. Wireless Personal Communications. 2022. № 126. P. 1–20. DOI 10.1007/s11277-022-09745-4.
- Bhavya V.S., Ketha S., Dr. Arathi R.S. Interference mitigation using adaptive digital beam-forming for 5G applications. International research journal of engineering and technology. 2023. V. 10. № 07. P. 864–866.
- Aswoyo B., Milchan M., Budikarso A. Adaptive beamforming based on linear array antenna for 2.3 GHz 5G communication using LMS algorithm. International electronics symposium (IES). 2022. P. 436–441.
- Whipple A., Ruzindana M., Burnett M., Kunzler J., Lyman K., Jeffs B., Warnick K. Wideband Array Signal Processing with Real-Time Adaptive Interference Mitigation. Sensors. 2023 V. 23. № 14. P. 6584. DOI 10.3390/s23146584.
- Noori N. Optimasation of a wideband tapped-delay lone array antenna. Iranian Journal of Electrical and Electronic Engineering. 2014. V. 10. № 2. P. 91–95.
- Makarenkov V.V., Kupriyanov N.A., Liferenko V.D., Lutsko I.S., Vasiliev S.V. Adaptive beamforming algorithm for a phased antenna array using the discrete Karhunen-Loev decomposition. Electromagnetic waves and electronic systems. 2023. V. 28. № 4. P. 48−56. DOI 10.18127/j15604128-202304-05. (in Russian)
- Hema N., Kidav J., Lakshmi B. VLSI Architecture for Broadband MVDR Beamformer. Indian Journal of Science and Technology. 2015. V. 8. P. 19–28.
- Chen Y., Lee J. Performance evaluation of DFT beamformers for broadband antenna array processing. Progress in Electromagnetics Research. 2013. V. 139. P. 57–86.
- Berkun R., Cohen I., Benesty J. Combined beamformers for robust broadband regularized superdirective beamforming. IEEE/ACM transactions on audio, speech and language processing. 2015. V. 23. № 5. P. 877–886.
- Ermolayev V.T., Sorokin I.S., Flaksman A.G., Semenov V.Y., Yastrebov A.V. Regularization of the Weight Vector of an Adaptive Antenna Array by Limiting the Number of the Basis Vectors. Radiophysics and Quantum Electronics. 2015. V. 58. № 3. P. 216–223. DOI 10.1007/s11141-015-9595-0.
- Ermolaev V.T., Semenov V.Yu., Sorokin I.S., Flaksman A.G. Application of the power-vector method for adaptive processing of signals in multibeam antenna arrays. Izvestiya vysshikh uchebnykh zavedeniy. Radiophysics. 2016. V. 59. № 10. P. 948–955. (in Russian)
- Maksimov M.V. Protection from radio interference. Moscow: Sovetskoe radio. 1976. 496 p. (in Russian)
- Widrow B., Glover J.R., McCool J.M., Kaunitz J., Williams C.S., Hearn R.H., Zeidler J.R., Eugene Dong Jr., Goodlin R.C. Adaptive noise cancelling: Principles and applications. Proceedings of the IEEE. 1975. V. 63. № 12. P. 1692–1716. DOI 10.1109/PROC.1975.10036.
- Tikhonov A.I., Arsenin V.Ya. Methods of solving ill-posed problems. Moscow: Nauka. 1979. 288 p. (in Russian)
- Voevodin V.V. Linear algebra. Moscow: Nauka. 1980. 400 p. (in Russian)

