Abstract: The sixth-generation fighter has superior stealth performance, but for the traditional kernel density estimation (KDE), precision requirements are difficult to satisfy when dealing with the fluctuation characteristics of complex radar cross section (RCS). To solve this problem, this paper studies the KDE algorithm for F/AXX stealth fighter. By considering the accuracy lack of existing fixed bandwidth algorithms, a novel adaptive kernel density estimation (AKDE) algorithm equipped with least square cross validation and integrated squared error criterion is proposed to optimize the bandwidth. Meanwhile, an adaptive RCS density estimation can be obtained according to the optimized bandwidth. Finally, simulations verify that the estimation accuracy of the adaptive bandwidth RCS density estimation algorithm is more than 50% higher than that of the traditional algorithm. Based on the proposed algorithm (i.e., AKDE), statistical characteristics of the considered fighter are more accurately acquired, and then the significant advantages of the AKDE algorithm in solving cumulative distribution function estimation of RCS less than 1 m2 are analyzed.

Key words: radar cross section (RCS), kernel density estimation (KDE), statistical properties

摘要： 第六代战斗机具有优越的隐身性能，但传统的核密度估计（KDE）在处理复杂雷达截面（RCS）波动特性时难以满足精度要求。为了解决这一问题，研究了F/A-XX隐身战斗机的核密度估计算法。针对现有固定带宽算法精度不足的问题，提出了一种基于最小二乘交叉验证和误差平方积分准则的自适应核密度估计算法（AKDE）。同时，根据优化后的带宽得到自适应雷达截面密度估计。最后，仿真验证了自适应带宽雷达截面密度估计算法的估计精度比传统算法提高50%以上。基于所提出的算法（即AKDE），更准确地获取了所考虑战斗机的统计特征，并分析了AKDE算法在求解雷达截面小于1 m2的累积分布函数估计方面的显著优势。

关键词: 雷达截面，核密度估计，统计特性