作者: Jun Fang , Tiantian Tuo , Bing Zeng , Huiping Duan
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摘要: In underdetermined direction-of-arrival (DOA) estimation using the covariance-based signal models, computational complexity turns into a noticeable issue because of high dimension virtual array manifold. this paper, real-valued Khatri-Rao (KR) approaches are developed on uniform linear (ULA) and nested array. The complexities subspace decomposition spectral search reduced compared with complex-valued KR approach. By designing special transformation matrix, influence noise is removed in mean time while data transformed from complex domain to real domain. Deploying sensors nonuniform spacings can raise degree freedom (DOF) hence help detect more sources situation. To increase DOF further, new geometry designed. denoising approach resolve complexities. performance improvement demonstrated by numerical studies.