Abstract
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.
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