RECENTLY, many practical problems with the uncertain feature, referred as uncertainty quantification (UQ), have received unprecedented attentions [1]. Due to the existence of uncertainty, the radar cross section (RCS) data obtained from dynamic/static measurement or electromagnetic simulation always have some mutual difference, which is even obvious sometimes. This leads to the question about the reliability of the dynamic targets’ RCS, which has also become a bottleneck in RCS research. Therefore, the uncertainty issue is not only an inevitable problem but also a valuable study topic in this field. Extensive efforts have been devoted to analyze and evaluate RCS measurement uncertainty [2], [3]. However, in the measurement process, results are often interfered by a variety of factors simultaneously, such as attitude disturbance of dynamic targets, system noise, frequency drift, environmental clutter, and so on. It is difficult to extract the impact of one of the abovementioned uncertainties from the measurement data. Therefore, there is a great necessity to study the impact of the specific uncertainty on RCS data by means of simulation. The RCS simulation of complex target is a complicated computational process which needs to take many practical factors into account, such as sheltering, multiple scattering [4], and so on. Such simulation systems are difficult to describe with all-inclusive mathematical models. Hence, the reliability of the dynamic RCS simulation has to be considered. A more feasible way of RCS UQ is the sampling-based statistical methods. Monte Carlo (MC) method is considered to be one of the most popular UQ methods [5], [6]. Its simplicity and nonintrusive characters simplify the implementation by repeating uncertain experiments and sample statistics with sufficient amounts. Although the convergence efficiency of such method is relatively low, it is widely used in the analysis of various complex electromagnetic problems because of its strong adaptability and less constraint on conditions. These advantages of MC method make it a good choice in UQ study, since it is a classical samplingbased statistical approach, the comparison and evaluation of numerical simulation data are critical for UQ.