Direction-of-Arrival Estimation for CS-MIMO Radar Using Subspace Sparse Bayesian Learning

Abstract : We address the problem of direction-of-arrival (DOA) estimation for compressive sensing based multiple-input multiple-output (CS-MIMO) radar. The spatial sparsity of the targets enables CS to be desirable for DOA estimation. By discretizing the possible target angles, a overcomplete dictionary is constructed for DOA estimation. A structural sparsity Bayesian learning framework is presented for support recovery. To improve the recovery accuracy and speed up the Bayesian iteration, a subspace sparse Bayesian learning algorithm is developed. The proposed scheme, which needs less iteration steps, can provides high precision DOA estimation performance for CS-MIMO radar, even at the condition of low signal-to-noise ratio and coherent sources. Simulation results verify the usefulness of our scheme.
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Yang Bin, Huang Dongmei, Li Ding. Direction-of-Arrival Estimation for CS-MIMO Radar Using Subspace Sparse Bayesian Learning. 9th International Conference on Intelligent Information Processing (IIP), Nov 2016, Melbourne, VIC, Australia. pp.31-38, ⟨10.1007/978-3-319-48390-0_4⟩. ⟨hal-01614998⟩

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