基于自适应径向基网络的舰船RCS统计特征识别方法

RCS Statistical Feature Recognition Method for Naval Vessels Based on Adaptive Radial Basis Network

文章采用舰船RCS频域起伏序列的均值、标准差为识别特征向量,利用提出的基于样本密度的自适应径向基网络,进行舰船分类识别研究。自适应径向基网络采用改进的自适应PSO方法估计样本密度最优邻域半径,实现径向基网络中心的自适应选择。改进的自适应PSO方法采用能反映样本聚类特点的BWP指标为适应度评价函数,采用快慢结合的高斯自适应惯性权重调节策略,提高了最优样本密度邻域半径的搜索速度和精度。实验结果表明,自适应径向基网络能自适应获得径向基网络最优识别率对应的RBF中心及其位置分布,减少了对建模人员经验的依赖,提高了反舰导弹对舰船类型的识别分类能力。

In this paper, we take the mean and standard deviation of the RCS frequency domain fluctuations was taked as the recognition feature, the adaptive radial basis network based on sample density was used to do research on ship recognition and classification. The improved adaptive PSO method was used to estimate the optimal neighborhood radius of sample density, which realized the adaptive selection of radial basis network center. The improved adaptive PSO method improved the search speed and precision of the neighborhood radius of the optimal sample density. It took BWP as the fitness evaluation function, which could reflect sample clustering features, and adopted Gauss adaptive inertia weight adjustment strategy. The experimental results showed that the adaptive radial basis network could adaptively obtain the RBF center and its position distribution corresponding to the optimal identification rate of radial basis network. Therefore, it reduced the dependence on the experience of modeling person, and enhanced the ability of the anti-ship missile to recognize the type of ship.