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SPARSITY-BASED MULTI-TARGET DIRECT POSITIONING ALGORITHM BASED ON JOINT-SPARSE RECOVERY

By W. Ke and L. Wu

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Abstract:
The direct position determination (DPD) method can improve the location accuracy compared with the traditional two-step location methods due to omitting the intermediate procedure of estimating the measurement parameters. However, the DPD methods presented so far are significantly more complex than the two-step approach. To overcome the shortcomings of the published DPD algorithms, a novel multi-target direct localization approach is firstly proposed by exploiting the jointly sparse property in the discrete spatial domain. The main idea of this paper is that the location estimation can be obtained by finding the sparsest solution according to the predefined overcomplete basis. Furthermore, the locations of targets can be obtained from noisy signals, even if the number of targets is not known a priori. Experimental results demonstrate that the proposed algorithm has superior positioning accuracy to other DPD methods and improves computational efficiency greatly.

Citation:
W. Ke and L. Wu, "Sparsity-Based Multi-Target Direct Positioning Algorithm Based on Joint-Sparse Recovery," Progress In Electromagnetics Research C, Vol. 27, 99-114, 2012.
doi:10.2528/PIERC11110704

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