A recursive algorithm for estimating the correlation matrix of the interference based on the QR decomposition (\textbf{Retracted})
Žurnal Sibirskogo federalʹnogo universiteta. Matematika i fizika, Tome 13 (2020) no. 2, pp. 160-169.

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Many tasks of digital signal processing require the implementation of matrix operations in real time. These are operations of matrix inversion or solving systems of linear algebraic or differential equations (Kalman filter). The transition to the implementation of digital signal processing on programmable logic device (FPGAs), as a rule, involves calculations based on the representation of numbers with a fixed point. This makes solving spatio-temporal processing problems practically impossible based on conventional computational methods. The article discusses the implementation of spatial-temporal signal processing algorithms in satellite broadband systems using QR decomposition. The technologies of CORDIC computations required for recurrent QR decomposition when used together in systolic algorithms are presented.
Keywords: phased antenna array, adaptive algorithms, Kalman filter, recursive least squares algorithm (RLS), systolic algorithm.
Mots-clés : QR decomposition
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Valery N. Tyapkin; Dmitry D. Dmitriev; Andrey B. Gladyshev; Peter Yu. Zverev. A recursive algorithm for estimating the correlation matrix of the interference based on the QR decomposition (\textbf{Retracted}). Žurnal Sibirskogo federalʹnogo universiteta. Matematika i fizika, Tome 13 (2020) no. 2, pp. 160-169. http://geodesic.mathdoc.fr/item/JSFU_2020_13_2_a3/

[1] Ya.D.Shirman, Radio-electronic systems: Fundamentals of construction and theory. Directory, Ed. 2nd, rev. and add., Radio Engineering, M., 2007 (in Russian)

[2] V.N.Tyapkin, I.N.Kartsan, D.D.Dmitriev, S.V.Efremova, “Algorithms for adaptive processing of signals in a flat phased antenna array”, SIBCON 2017

[3] A.I.Perov, Statistical theory of radio systems, Radio Engineering, M., 2003 (in Russian)

[4] I.N.Kartsan, V.N.Tyapkin, D.D.Dmitriev, A.E.Goncharov, P.V.Zelenkov, I.V.Kovalev, IOP Conf. Series: Materials Science and Engineering, 155:1 (2016) | DOI

[5] I.N.Kartsan, V.N.Tyapkin, D.D.Dmitriev, A.E.Goncharov, I.V.Kovalev, IOP Conf. Series: Materials Science and Engineering, 255 (2017) | DOI

[6] V.N.Tyapkin, D.D.Dmitriev, Yu.L.Fateev, N.S.Kremez, J. Sib. Fed. Univ. Math. $\$ Phys., 9:2 (2016), 258–268 | DOI | MR

[7] I.N.Kartsan, Y.L.Fateev, V.N.Tyapkin, D.D.Dmitriev, A.E.Goncharov, P.V.Zelenkov, I.V.Kovalev, IOP Conf. Series: Materials Science and Engineering, 155:1 (2016) | DOI

[8] R.A.Monzingo, T.W.Miller, Introduction to Adaptive Arrays, John Wiley $\$ Sons, New York, 1980

[9] Ya.D.Shirman et al., “The first domestic studies of the adaptation of antenna systems to interfering influences”, Radio engineering, 11, M., 1989 (in Russian)

[10] V.N.Tyapkin, D.D.Dmitriev, V.G.Konnov, A.N.Fomin, “A method for determining the vector of spectral coefficients by the likelihood ratio criterion”, Bulletin of the Siberian state Aerospace University named after Acad. M. F. Reshetneva, 43:3 (2012), 76–79

[11] V.I.Dzhigan, “Equivalence conditions for recursive adaptive filtering algorithms by the least squares criterion”, Telecommunications, 6 (2006), 6–11 (in Russian)

[12] I.A.Lubkin, V.N.Tyapkin, “The use of recurrent adaptive algorithms to solve the problem of suppressing active noise interference in satellite communication systems”, Bull. Sib. state Aerospace Univ. named after Acad. M.F. Reshetneva, 28:2 (2010), 39–43 (in Russian)

[13] G.H.Golub, C.F.Van, Lone Matrix calculations, The Johns Hopkins University Press, Baltimor, 1996 | MR

[14] D.T.M.Slock, T.Kailath, “Numerically stable fast transversal filters for recursive least squares adaptive filtering”, IEEE Trans. Signal Processing, 39:1 (1991), 92–114 | DOI | MR | Zbl

[15] A.Benallal, A.Gilliore, “A new method to stabilize fast RLS algorithm based on the first-order model of the propagation of numerical errors”, Proc. Int. Conf. on Acoustic, Speech and Signal Processing, v. 5, 1988, 1373–1376

[16] J.G.McWhirter, “Recursive least-squares minimization using a systolic array”, Proceedings of the SPIE, 0431, The Intern. Sic. Opt. Eng., 1983, 105–112 | DOI