The distribution of aircraft dynamic radar cross section (RCS) varies at different course short-cuts and heights and can behave as a bimodal shape. Classical models, such as the Weibull distribution, the chi-square distribution, and lognormal distribution, fit poorly with bimodal RCS distributions and are unable to accurately describe the statistical characteristics of aircraft dynamic RCS. In order to overcome the shortcoming of the classical models, we propose an exponential polynomial distribution model as the probability density function for studying the RCS statistical characteristics. The model parameters were acquired through the linear least-square method. Based on the RCS statistical data, the proposed distribution model was validated through simulation experiments. We calculated the fitting error between the new model and the RCS statistical distribution and determined the optimal exponential polynomial distribution model. We then computed the coefficients of the determination to evaluate the proposed model. The Kolmogorov–Smirnov test showed that the proposed approach yielded smaller values compared to conventional distribution models. For all the analyzed cases, the exponential polynomial model can fit very well against the RCS distribution data with bimodal shape, and the fitting effect is significantly better than the results of the three classical models. The advantages of the proposed model are further demonstrated by comparing it with the Gaussian mixture distribution model from fitting error, determination coefficient, goodness-of-fit, mean error and variance error. The results suggest that the exponential polynomial distribution model provides a more effective alternative in describing the fluctuation characteristics of aircraft dynamic RCS at the different course short-cuts and heights. Given the advantages exhibited by the exponential polynomial distribution model in this study, the methodology and findings in this study can be used to improve in early warning detection of radar targets and as a theoretical basis to enhance the overall performance of radars. 2009 Elsevier Ltd. All rights reserved.

飞机动态雷达截面 (RCS) 的分布在不同的航向捷径和高度上有所不同，并且可以表现为双峰形状。经典模型，如威布尔分布、卡方分布和对数正态分布，与双峰 RCS 分布拟合不佳，无法准确描述飞机动态 RCS 的统计特性。为了克服经典模型的不足，我们提出了一种指数多项式分布模型作为概率密度函数来研究RCS的统计特性。模型参数通过线性最小二乘法获得。基于RCS统计数据，通过仿真实验验证了所提出的分布模型。计算新模型与RCS统计分布的拟合误差，确定最优指数多项式分布模型。然后我们计算了确定的系数来评估所提出的模型。Kolmogorov-Smirnov 检验表明，与传统分布模型相比，所提出的方法产生的值更小。对于所有分析的情况，指数多项式模型可以很好地拟合具有双峰形状的RCS分布数据，拟合效果明显优于三个经典模型的结果。通过与高斯混合分布模型的拟合误差、决定系数、拟合优度、均值误差和方差误差的比较，进一步证明了该模型的优点。结果表明，指数多项式分布模型为描述不同航向捷径和高度下飞机动态RCS的波动特性提供了一种更有效的替代方法。鉴于本研究中指数多项式分布模型所表现出的优势，本研究的方法和发现可用于改进雷达目标的早期预警检测，并作为提高雷达整体性能的理论基础。2009 Elsevier Ltd. 保留所有权利。本研究的方法论和研究结果可用于改进雷达目标的早期预警检测，并作为提高雷达整体性能的理论基础。2009 Elsevier Ltd. 保留所有权利。本研究的方法论和研究结果可用于改进雷达目标的早期预警检测，并作为提高雷达整体性能的理论基础。2009 Elsevier Ltd. 保留所有权利。