Smart antenna design for real-time multi-channel power spectral density estimation and target localization

Situational awareness is dependent on efficient spectrum use for data communication. In this paper, we describe spectrum band selection based on the target operations and localization. For wireless spectrum detection, given the system noise and signal information, the Neyman-Pearson based likelihood ratio test can provide the optimal detection performance under a certain probability of false alarms. However, in practice not all the information of alternative hypotheses are available. In this paper, a robust generalized likelihood ratio test (RGLRT) based detection is proposed without knowing channel information and signal information. An online subspace learning algorithm for direction of arrival (DOA) is introduced, which only uses fixed partial observation of antennas to estimate the subspace of the steering matrix. The subspace rank is not necessarily known at the beginning. The simulation results show that only partial observations can achieve a good DOA estimation performance with comparatively smaller estimation error.