Aiming at the unknown clutter intensity in multiple tracking, online estimation algorithm of clutter intensity based
on entropy distribution is proposed. The clutter intensity is modeled by finite mixture model. The entropy distribution, which
depends only on mixing weights, is adopted as the prior distribution of mixing parameters. The online estimation formulation
of mixing weight is derived by Lagrange multiplier in the sense of maximum a posterior. Stochastic approximation procedure
is regarded as the strategy of online estimation of component mean and covariance. The online estimation formulations of
component mean and covariance are derived based on missing data. Simulation results show that the online estimation
algorithm of clutter intensity based on entropy distribution improves the performance of probability hypothesis density filter
in multiple target tracking.