The invention discloses an
underdetermined blind source separation method based on density, mainly solving the problems that the
computation complexity of the prior art is high, the prior art is easily influenced by an initial value and the number of source signals needs to be given. The method comprises the following steps: after the low energy sampling data of an observed
signal is removed, projecting the observed
signal onto a right half unit
hypersphere; computing the density parameters of all projective points, and deleting the projective points with smaller density; utilizing an improved K-mean value clustering
algorithm to cluster the surplus projective points and determining the optimal clustering number and clustering center; removing the cluster with less number of
data objects, wherein the number of the surplus clusters is the estimated value of the number of source signals, and the corresponding clustering center is the estimated value of each
column vector of a
confusion matrix; and according to the observed
signal and the estimated
confusion matrix, adopting a
linear programming method to recover the source signals. According to the method, the
computation complexity is reduced, the influence of the initial value on the estimated performance is reduced, the
confusion matrix can be estimated when the number of the source signals is unknown, and the estimating precision of the
confusion matrix and the source signals can be increased.