For the purpose of computational efficiency, we propose two subspace-based methods, but without eigendecomposition, to address the two typical problems in nested array processing, i.e., direction-of-arrival (DOA) esti...
详细信息
ISBN:
(纸本)9798350344868;9798350344851
For the purpose of computational efficiency, we propose two subspace-based methods, but without eigendecomposition, to address the two typical problems in nested array processing, i.e., direction-of-arrival (DOA) estimation and noise elimination. In detail, to estimate DOA parameters, we judiciously arrange the segments extracted from the co-array model and then introduce a novel co-array-based orthogonal propagator method (coPM). Next, we develop a projection-based noise cancellation approach in the co-array domain, improving the relatively poor performance of coPM at low signal-to-noise ratios. Simulations evaluate the proposed algorithms under both overdetermined and underdetermined conditions.
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