基于模糊支持向量机的一维距离像识别

One-dimensional Range Profile Recognition Based on FSVM

在毫米波雷达一维距离像识别中,为达到消除噪声与孤立点影响的目的,在研究了基于距离和样本紧密度的隶属度函数的基础上,提出了一种能反映雷达回波信号在特征空间分布的隶属度函数。用一个紧凑的超椭球体将样本包围起来,并使用超椭球体的方向半径度量样本之间的紧密度。将改进的基于样本紧密度的隶属度函数应用于模糊支持向量机中,可较好地区分开支持向量与含噪声或野值的样本。实验结果显示:随着雷达回波信号信噪比的降低,支持向量机的目标识别率迅速下降,而模糊支持向量机的目标识别率只下降了1%,有效地减小了噪声对一维距离像识别结果的影响。

Based on the discussion of membership functions computed by distance and affinity among samples, a new membership function is introduced which can reflect the distribution of radar echo signal in feature space, in order to avoid the influence of noise and isolated points in millimeter-wave radar one-dimensional range profile recognition. Super-ellipse is introduced to improve membership function and applied to fuzzy support vector machine （FSVM） to distinguish support vectors with outliers or noise. Compact super-ellipse is used to surround samples and its direction radius can measure the affinity between samples. The experimental results show that the recognition rate of support vector machine drops down rapidly with the decrease of signal-noise-ratio for echo waves, but only one percent of FSVM reduces, which reduces the influence of noise on recognition results of one-dimensional range profiles effectively.