Underdetermined blind identification method based on general covariance and tensor decomposition

Abstract

The invention relates to the field of signal identification, in particular relates to the field of blind source/signal separation and specifically relates to an underdetermined blind identification method based on general covariance and tensor decomposition. The underdetermined blind identification method based on general covariance and tensor decomposition comprises establishing kernel functional equation sets according to the general covariance matrix of observation hybrid sampled data, stacking the kernel functional equation sets into a three-dimensional tensor model, and finally gaining a factor matrix by virtue of tensor Tucker decomposition, thereby identifying underdetermined hybrid system characteristics or hybrid matrixes. The underdetermined blind identification method based on general covariance and tensor decomposition has the technical advantages of effective performance improvement and relatively low computation complexity in contrast with a mixed matrix identification method in traditional underdetermined blind source separation; the underdetermined blind identification method provides a technical foundation for the blind anti-jamming technique of array signal processing and satellite communication.

Description

technical field [0001] The invention relates to the field of signal identification, in particular to the field of blind source / signal separation. Background technique [0002] Blind source / signal separation has important technical advantages when applied to wireless communication systems: it reduces the use of pilots and improves the spectral efficiency of the system; it relaxes the dependence on prior information and avoids tedious pilot-based channels Estimation, at the same time, can overcome the performance damage caused by the estimation error, and then improve the robustness of the source signal recovery; it can separate the mixed signals that overlap each other in the time-frequency domain, which is of great significance for the anti-co-channel interference in wireless communication. [0003] Blind source / signal separation refers to extracting or separating unknown source signals and identifying unknown mixed system characteristics, namely the mixing matrix, only from...

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Problems solved by technology

[0009] In addition, the above two traditional methods are based on the standard decomposition form of tensors. In order to obtain better statistical information, the dimensions of the tensors that need to be constructed are generally large, so the decomposition complexity is relatively high.

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