A simple and feasible post-processing method for passive localization is proposed.Post-processorof passive localization concerns estimating the time-varying state of a dynamic system.The generalway to fulfill this is Kalman filtering.This paper applies the Linear Minimum Variance(LMV)method,generally used for parameter estimation,to the time-verying state estimation of a linear dynamic system.So this new method can be called LMV filter.In fact,it is an averaging method,becuse LMV filtertakes weighted average of K samples of observation with different weighting coefficients which aregiven by a system of equations.Two computer simulation results are presented to show that therange estimate convefges fast and is good in performance,no divergence appears and the method hasthe capacity to adapt to the target maneuver.Another important feature is its very low computa-tional level,useful in poor computer facility case. A simple and feasible post-processing method for passive localization is proposed. Post-processor of passive localization accounts estimating the time-varying state of a dynamic system. The generalway to fulfill this is Kalman filtering. This paper applies the Linear Minimum Variance (LMV) method, generally used for parameter estimation, to the time-very state estimation of a linear dynamic system. So this new method can be called LMV filter.In fact, it is an averaging method, becuse LMV filtertakes weighted average of K samples of observation with different weighting coefficients which aregiven by a system of equations. Two computer simulation results are presented to show the therange estimate convefges fast and is good in performance, no divergence appears and the method hasthe capacity to adapt to the target maneuver. Another important feature is its very low computa-tional level, useful in poor computer facility case.