Conventional high resolution range profile (HRRP) based radar automatic target recognition (RATR) methods are based on a complete template library assumption. However, such an assumption can barely hold in real world applications since it is difficult to observe target echoes from all aspects, especially for noncooperative targets. To alleviate the target-aspect missing problem, we develop a domain-aware meta network (DOAMN) for HRRP-based RATR. Specifically, the DOAMN first uses a domain-aware (DOM) module to distinguish whether HRRPs come from the seen or unseen target aspects, then a meta network (MNet) is employed to learn a generalized parameter initialization that is able to achieve fast adaptation among target aspects. To enable the proposed DOAMN to be trained in an end-to-end manner, we further present an effective iterative hybrid optimization method. Experiments on simulated HRRP dataset demonstrate the effectiveness and efficiency of the proposed model.

传统的基于高分辨率距离剖面 (HRRP) 的雷达自动目标识别 (RATR) 方法基于完整的模板库假设。然而，这样的假设在现实世界的应用中几乎不能成立，因为很难从各个方面观察目标回波，特别是对于非合作目标。为了缓解目标方面缺失问题，我们为基于 HRRP 的 RATR 开发了一个域感知元网络（DOAMN）。具体来说，DOAMN 首先使用域感知 (DOM) 模块来区分 HRRP 是来自已见或未见的目标方面，然后使用元网络 (MNet) 来学习能够实现快速适应的广义参数初始化。目标方面。为了使提议的 DOAMN 能够以端到端的方式进行训练，我们进一步提出了一种有效的迭代混合优化方法。在模拟 HRRP 数据集上的实验证明了所提出模型的有效性和效率。