The existing spectrum detection method can not take full advantage of angle dimension. To sense the spectrum more comprehensively, the signal model is established based on the sparsity of angle dimension. The reconstruction result can be derived by Sparse Bayesian Learning (SBL) algorithm. By integrating the binary probability hypothesis into iterative procedure of SBL, a decision test combined with adaptive threshold is derived. The proposed pruning step can accept the active components of the model, and transform the sparse recovery into a detection problem for signals from different angles. Therefore, the algorithm can sense the spectrum blindly with constant false-alarm rate as well as estimate the accurate angle of each incident signal. Numerical simulation results verify that adaptive threshold can improve reconstruction accuracy with low computing cost. Moreover, the proposed algorithm can achieve better estimation and detection performance than previous algorithms.
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