RADAR HRRP UNSEEN CLASS RECOGNITION BASED ON THE JOINT DICTIONARY LEARNING

Existing task settings and methods for radar high resolution range profile (HRRP) recognition are limited in addressing open challenges. To avoid labor-intensive data collection and model retraining, we formulate a new task called HRRP unseen class recognition, where the testing classes are unknown during training. To perform this task, we utilize metric learning to explore the potential information of unseen categories. Due to the target-aspect sensitivity problem of HRRP, feature extraction is a key step for recognition. Therefore, we take into account the time, frequency and aspect-angle characteristics of targets. Then a joint dictionary learning method is proposed to align different modalities in the latent common space to capture the intrinsic invariant representations of the unseen class targets. A variety of experiments demonstrate the effectiveness of our method.