P. N. Bashly1, V. G. Denisenko2, S. E. Mishchenko3, V. V. Shatsky4
1 Rostov Branch of Russian Customs Academy (Rostov-on-Don, Russia)
2–4 FSUE Rostov-on-Don Scientific Research Institute of Radio Communications (Rostov-on-Don, Russia)

3 mihome@yandex.ru

Two algorithms for singular value decomposition of the correlation matrix have been proposed for the implementation of angular super-resolution and the construction of the spatial spectrum of a digital antenna array. The first algorithm is based on the nonlinear iterative partial least squares (NIPALS) method, which is usually used to calculate the principal components of large amounts of data. In contrast to the main NIPALS algorithm, the possibility of recursively specifying initial approximations of eigenvectors based on the results of signal processing by a digital antenna array has been used. To process each subsequent correlation matrix, the eigenvectors of the previous processing phase have been used. The second algorithm is based on the parallel Jacobi algorithm and is distinguished by the use of analytical relations to calculate complex rotation matrices. It has been shown that the recursive assignment of the initial approximations of the eigenvectors in the first algorithm made it possible to significantly reduce the required number of iterations. This makes the first algorithm potentially more efficient than the Jacobi algorithm. In turn, the Jacobi algorithm is more resistant to limiting the length of the mantissa. A simulation has been performed confirming the operability and convergence of the proposed MUSIC algorithms based on two different singular expansions with the resolution of two closely spaced sources.

Bashly P.N., Denisenko V.G., Mishchenko S.E., Shatsky V.V. Angular super-resolution MUSIC algorithms based on the NIPALS and Jacobi methods. Antennas. 2025. № 6. P. 6–14. DOI: https://doi.org/10.18127/j03209601-202506-01 (in Russian)

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