平移敏感性问题是机器学习方法应用在基于高分辨距离像的雷达目标识别领域的重要限制。在实际雷达工作场景中,高分辨距离像中的目标可能出现在高分辨距离像的不同位置,导致将高分辨距离像作为样本向量直接输入机器学习系统时不同样本的相同特征维度代表信息不统一,严重制约了机器学习系统的性能。针对这一问题,文中尝试建立高分辨距离像样本集对齐的目标函数,并给出该目标函数的近似解。在将高分辨距离像样本集进行对齐处理后,使用机器学习方法进行基于高分辨距离像的雷达目标识别的性能将得到显著提升。基于实测数据对该方法进行了性能验证,实验结果证实了该方法的有效性。

Translation sensitivity is one of the important limits of machine learning methods applied on radar target recognition based on high resolution range profiles. In the real radar situations, the targets are located on the different positions in the high resolution range profiles, leading to the result that the information represented by the same dimension of different high resolution range profile samples is not coherent with each other if the high resolution range profiles are regarded as the input of a machine learning system directly, which reduces the performance of the machine learning system. To solve the problem, a novel project function is proposed for high resolution range profile alignment, and solved by an approximation algorithm. Benefit from the alignment process, with machine learning methods, the performance of radar target recognition based on high resolution range profiles is improved significantly. The performance of the proposed method is validated based on measured data, and experimental results show the effectiveness of the proposed method.

TN957.51