Abstract: Disclosed is a method for classifying mixed signals, comprising: receiving mixed signals; performing calculation on a matrix corresponding to the mixed signals by means of a preset Principal Component Analysis method to obtain to-be-classified mixed signals and to determine the number of types of signals contained in the to-be-classified mixed signals; determining a separation matrix based on the number of types of signals contained in the to-be-classified mixed signals; separating individual signals in the to-be-classified mixed signals by means of the separation matrix to obtain to-be-identified signals; calculating a preset number of high-order cumulants corresponding to each to-be-identified signal in the to-be-identified signals respectively; taking the calculated high-order cumulants as characteristics of the to-be-identified signal corresponding to the high-order cumulants respectively; inputting the characteristics of the to-be-identified signal into a preset classification model; and obtaining a modu

Abstract: Disclosed is a method for classifying mixed signals, comprising: receiving mixed signals; performing calculation on a matrix corresponding to the mixed signals by means of a preset Principal Component Analysis method to obtain to-be-classified mixed signals and to determine the number of types of signals contained in the to-be-classified mixed signals; determining a separation matrix based on the number of types of signals contained in the to-be-classified mixed signals; separating individual signals in the to-be-classified mixed signals by means of the separation matrix to obtain to-be-identified signals; calculating a preset number of high-order cumulants corresponding to each to-be-identified signal in the to-be-identified signals respectively; taking the calculated high-order cumulants as characteristics of the to-be-identified signal corresponding to the high-order cumulants respectively; inputting the characteristics of the to-be-identified signal into a preset classification model; and obtaining a modu

Abstract: Clustering-based methods, apparatuses for frequency offset determination and elimination and electronic devices are disclosed. The clustering-based method for frequency offset determination includes: determining a constellation diagram for a received signal; determining N different values within a preset frequency interval, as N frequency offset estimates for a frequency offset of the received signal; for each of the frequency offset estimates, correcting the constellation diagram based on the frequency offset estimate to obtain a corrected constellation diagram, clustering signal points on the corrected constellation diagram, and calculating an area of a signal region in the corrected constellation diagram after clustering; and determining a frequency offset estimate corresponding to a signal region with a minimum area as a value of the frequency offset. The embodiments of the present invention can improve the accuracy and stability of the calculated value of the frequency offset.

Abstract: Clustering-based methods, apparatuses for frequency offset determination and elimination and electronic devices are disclosed. The clustering-based method for frequency offset determination includes: determining a constellation diagram for a received signal; determining N different values within a preset frequency interval, as N frequency offset estimates for a frequency offset of the received signal; for each of the frequency offset estimates, correcting the constellation diagram based on the frequency offset estimate to obtain a corrected constellation diagram, clustering signal points on the corrected constellation diagram, and calculating an area of a signal region in the corrected constellation diagram after clustering; and determining a frequency offset estimate corresponding to a signal region with a minimum area as a value of the frequency offset. The embodiments of the present invention can improve the accuracy and stability of the calculated value of the frequency offset.