基于输出信杂噪比的机载雷达训练样本选择算法

Output SCNR-Based Training Samples Selection Method For Airborne Radar

在非均匀杂波环境下,被干扰目标污染的训练样本会严重影响空时自适应处理(space-time adaptive processing,STAP)性能,需进行剔除。该文提出一种基于输出信杂噪比(signal-to-clutterplus-noise ratio,SCNR)的训练样本选择算法,以输出SCNR值作为检验统计量进行样本筛选,当样本的杂波特性与目标距离环(cell under test,CUT)越相近,则基于样本设计的STAP滤波器对CUT的杂波抑制性能就越好,输出SCNR越高。此外,该文利用子孔径协方差矩阵直接表征CUT和样本的杂波特性,可确保各距离环杂波特性的表征不受其他距离环的影响,且准确性不受样本数量的限制。最后,通过实测数据验证了该样本筛选算法的性能。

In heterogeneous clutter environment,the training samples contaminated by outliers seriously degrade the performance of space-time adaptive processing(STAP)and needs to be eliminated.Therefore,this paper proposes an output signal-to-clutter-plus-noise ratio(SCNR)based training sample selection algorithm.The output SCNR is acted as the test statistic for training samples selection.When the clutter property of the training sample is more similar to the cell under test(CUT),the clutter suppression performance is better,and the output SCNR is higher.Moreover,this paper exploits the subaperture’s covariance matrix to directly characterize the clutter property of each range cell,ensuring the characterization is not affected by others,and the estimation accuracy does not depend on the number of the samples.Finally,the numerical experiments with measured data demonstrate the performance of the proposed training samples selection method.