Abstract: Expectation-Maximization (EM) iteration is one of the most efficient algorithms for parameter estimation for Gaussian mixture model, which is a characteristic probability density function model for nonGaussian processes. In general, EM iteration for multi-dimensional Gaussian mixture is too complicated to realize in practice. Fortunately, for fitting of the background′s probability density function in active detection, the singledimensional Gaussian mixture is adequate. Therefore, EM iteration can be simplified efficiently. In view of active detection, followed with descriptions of single-dimensional Gaussian mixture model and its parameter estimation problem, a practicable simplified EM iteration is derived. And easily programmable flowchart is proposed. Initialization is important in EM iteration. Incorrect initialization may lead to wrong convergence to improper local extreme points of the likelihood function. Three schemes for initialization are proposed for high calculating speed, high estimation accuracy, and for the compromise of the two cases. Their applications are discussed and, finally, a numerical example is given.