Hybrid matrix recognition method in underdetermined blind source separation based on tensor regular decomposition

Abstract

The invention discloses a hybrid matrix recognition method in underdetermined blind source separation based on tensor regular decomposition. The method mainly solves the problem that in the prior art, hybrid matrix estimation is limited by specific conditions. The method includes the implementation steps that (1), source signals are sampled and observation data are acquired; (2), four-order covariance matrixes in different time delays are calculated through four-order cumulants of the observation data; (3), the four-order covariance matrix in different time delays are expanded into a three-order tensor mode; (4), tensor regular decomposition is conducted on three-order tensors, and a Khatri-Rao product matrix of a hybrid matrix to be recognized is acquired; (5), the product matrix is processed through an eigenvalue decomposition method, and an estimated value of the hybrid matrix is acquired. The method has the advantage of being high in recognition accuracy and can be used in the fields of voice, communication, radar and biomedicine and used for underdetermined blind source separation under the time-frequency aliasing condition.

Description

Technical field [0001] The invention belongs to the field of communication technology, and particularly relates to a hybrid matrix identification method, which can be used in under-determined blind source separation of source signals in the fields of speech, communication, radar and biomedicine under time-frequency aliasing conditions. Background technique [0002] Blind source separation BSS refers to the purpose of separating the source signal only through the observation signal received by the sensor under the condition of unknown transmission channel and source signal. This method has been widely used in speech signal processing, image processing, radar, Various fields such as communication and biomedicine. As a classic algorithm for blind source separation, independent component analysis ICA and its extended algorithms are mostly used to solve problems when the number of observation signals is equal to or greater than the number of source signals. This kind of blind source s...

AI Technical Summary

Problems solved by technology

Some scholars take advantage of the sparsity of the signal and use the clustering method to identify the mixing matrix. When the source signal does not meet the sparsity in the time domain, they use tools such as Fourier transform or wavelet transform to transform the signal into a sparse frequency domain. , and then use the method of clustering or potential function to identify the mixing matrix, for example, NgutyenLin-Trung, ABelouchrani, KarimA-M. In the case of uniform aliasing, the performance of this method is not ideal

Some scholars use time-frequency methods, for example, Lu Fengbo, Huang Zhitao, Peng Geng, etc., "Undetermined Blind Aliasing Separation Based on Time-Frequency Distribution", Journal of Electronics, 2011, 39(9), pp.2067-2072, the The method performs time-frequency processing on the observed signal, and then extracts the self-source time-frequency points of the signal, uses the self-source time-frequency points to construct a tensor model and decomposes the model by tensor regularization, so as to complete the identification of the mixing matrix, but in the frequency domain In the case of serious overlap, the extraction of time-frequency points from the source is not ideal, so it will affect the recognition performance of the mixing matrix; some scholars use the statistical characteristics of the signal, such as DeLathauwerL, CastaingJ, CardosoJ, "Fourth-order cumulant- basedblindidentificationofunderdeterminedmixtures”, IEEETransaction on Signal Processing, 2007, 55(6), pp.2965-2973, this method does not require the source signal to meet the sparse characteristics, only the source signal is a statistically independent non-Gaussian signal, in the actual process, this condition is often It is easy to satisfy, but in the process of solving the algorithm, it needs to assume that the source signal has the same sign of kurtosis, that is, the numerical statistic reflecting the distribution characteristics of the vibration signal, which is the normalized fourth-order central moment, while the source signal first In the case of insufficient empirical knowledge, this condition is often difficult to meet, thus affecting the recognition accuracy of the mixing matrix

Images