Algorithms for Adaptive Signal Processing in Geosynchronous Orbit Satellite Systems

Relevance. Adaptive signal processing is a key technology in modern satellite systems. Its use significantly improves the efficiency of radio engineering systems by improving interference immunity and increasing the operating range. Iterative adaptation algorithms are used to implement spatial filtering in real time. An analysis of existing developments shows that the vast majority of solutions are based on least mean squares (LMS) and recursive least squares (RLS) algorithms. The popularity of these methods is due to their relative simplicity of implementation and optimal characteristics in a stationary electromagnetic environment. However, in a dynamically changing signal-to-noise environment, their effectiveness decreases sharply, and in these conditions, non-stationary algorithms based on the Kalman filter are used, among which the most well-known are the constant modulus algorithm based on the unbiased Kalman filter (UKF-CMA) and the minimum variance distortionless algorithm based on the extended Kalman filter (EKF-MVDR).

The aim of the study was to improve the signal-to-noise ratio by using adaptive signal processing algorithms in geostationary satellite communication systems.

The work used methods of mathematical modeling of adaptive spatial filtering algorithms for satellite communication channels in the MATLAB environment.

In the solution of solving the scientific problem, an analysis of the stability of both stationary algorithms (LMS and RLS) and non-stationary algorithms based on Kalman filtering (UKF-CMA, EKF-MVDR, UKF-MVDR) in a geostationary satellite communication system for various environments, such as urban, suburban, and rural areas. An analysis of computational complexity, convergence speed, and signal-to-noise ratio gain was also performed for the algorithms under study in stationary and non-stationary signal-to-noise conditions.

The scientific novelty of this work lies in proposing a modification of the EKF-MVDR algorithm based on an unbiased Kalman filter (UKF-MVDR) to improve the stability of the algorithm in non-stationary signal-to-noise conditions as applied to adaptive signal processing tasks.

The theoretical significance of this work lies in the use of spatial signal processing algorithms in geostationary satellite communication systems to ensure stable operation in stationary and dynamic signal-to-noise environments.

1. Glushankov E., Korovin K., Boyko I., Borisov M. The Current State and Prospects for the Development of Adaptive Antenna Complexes in Radio Engineering Systems. Herald of SPbSUT. 2024;2(1):9. (in Russ.) EDN:AKVWWB

2. Grigoriev V.A., Shchesnyak S.S., Gulyushin V.L., Raspaev Yu.A., Khvorov I.A., Shchesnyak A.S. Adaptive Antenna Arrays. Part 1. St. Petersburg: ITMO-University Publ.; 2016. p. 55. (in Russ.)

3. Glushankov E.I., Kirik D.I., Korovin K.O., Tsarik I.V., Boyko I.A., Kazmin O.Yu. Implementation of the adaptive beamforming algorithm on FPGA. Modeling, Optimization and Information Technology. 2023;11(1):19–20. (in Russ.) DOI:10.26102/2310-6018/2023.40.1.025. EDN:JMTNCV

4. Shchedrin A., Boyko I. Using a Kalman Filter With Constraints to Reduce the Sensitivity of the Receiving Active Phased Array Antenna to the Angle. Herald of SPbSUT. 2024;2(4):4. (in Russ.) EDN:ENPYXH

5. Shchedrin A.A., Glushankov E.I., Boyko I.A. Researching Kalman filtering algorithms for adaptive generation of radiation pattern. Proceedings of the Scientific and Technical Conference of the St. Petersburg Scientific and Technical Association of Radio Electronics named after A.S. Popov, Dedicated to the Radio Day. 2024;1(79):3–5. (in Russ.) EDN:NHEOCZ

6. Shubber Z.A., Jamel T.M., Nahar A.K. Beamforming Array Antenna Technique Based on Partial Update Adaptive Algorithms. International Journal of Electrical and Computer Engineering Systems. 2023;14(8):821–831. DOI:10.32985/ijeces.14.8.1

7. Fu X.-Z, Liu Z., Hu S.-L., Liu Z.-K. Variable step-size LMS adaptive algorithm and its application in time delay estimation for low SNR. Journal of Central South University (Science and Technology). 2012;43(3):1010–1019.

8. Bhotto M.Z.A., Bajić I.V. Constant Modulus Blind Adaptive Beamforming Based on Unscented Kalman Filtering. IEEE Signal Processing Letters. 2015;22(4):474–478. DOI:10.1109/LSP.2014.2362932

9. Omgond P., Singh H. Constrained Kalman filter based interference suppression in phased arrays. Proceedings of the International Microwave and RF Conference, IMaRC, 15–17 December 2014, Bangalore, India. IEEE; 2014. p.286–289. DOI:10.1109/IMaRC.2014.7039029

10. Rec. ITU-R P.681-11. Propagation data required for the design systems in the land mobile-satellite service. 2019.