一种噪声背景下的雷达目标识别方法

Radar target recognition method under noise background

雷达高分辨距离像(high-resolution range profile,HRRP)包含了丰富的目标结构信息,在雷达目标识别领域有良好的应用前景。针对传统的HRRP识别方法对噪声环境适应性差的问题,选用具有时移不变性的紧支撑小波自相关作为支持向量机(support vector machine,SVM)分类器的核函数,研究了幂次变换(power transform,PT)参数的选取对识别效果的影响,给出了参数选取经验公式,结合信噪比实时估算自适应地进行数据预处理以增强算法的抗噪性能。仿真表明,所提出的方法与传统的高斯径向基核SVM相比,提高了目标识别率,并且具有较好的噪声稳健性。

Radar high-resolution range profile（HRRP） provide potentially discriminative information on the geometry of target,which has been shown to be promising signatures for radar automatic target recognition（ATR） application.As traditional algorithms are not robust to noise,an auto-correlation wavelet support vector machine is proposed,the kernel of which is constructed with a compactly supported wavelet satisfies the translation invariant property.Adaptive power transformation is adopted to enhance the classifier noise-robustness with the estimation method for real-time SNR and an empirical formula for selecting the power exponent.The simulation results show that the average recognition rate with the proposed classifier is higher than SVM with Gaussian RBF kernel under Gaussian white noise background.