Persymmetric Adaptive Union Subspace Detection - Pan, Liyan; Gao, Yongchan; Ye, Zhou; Lv, Yuzhou; Fang, Ming (Frontiers in Signal Processing, 2021)
This paper address the detection of a signal belonged to several possible subspace models, namely, a union of subspaces (UoS) , where the active subspace that generated the observed signal is unknown. By incorporating the persymmetric structure of received data, we propose three UoS detectors based on GLRT, Rao and Wald criteria to alleviate the requirement of training data. In addition, the detection statistic and classification bound for the proposed detectors are derived. Monte-Carlo simulations demonstrate the detection and classification performance of the proposed detectors over the conventional detector in training-limited scenarios.