Abstract
A method of super resolution DOA estimation with incomplete single snapshot data via matrix completion (MC) and compress sensing (CS) is presented. First, the incomplete single snapshot data is reshaped into a low-rank Hankel matrix form and the complete data can be reconstructed through matrix completion. Then, super resolution DOA estimation can be got through CS algorithm using these constructed complete data. Numerical simulations demonstrate that this method can get high accuracy DOA estimation with less number of array elements.
Highlights
DOA is a very important parameter in Radar system and its estimation accuracy will seriously affect the performance of Radar system
The interval between array elements is d .The signal of targets are incoming from M different addition to sparsity, the received signals have the property of low rank
This method is termed as the matrix completioncompress sensing (MC-CS) algorithm in this paper
Summary
Note that the CS method theory utilizes the inherent sparseness of targets to get high resolution. In Assume there is a uniform linear array (ULA)which has N receivers. The interval between array elements is d .The signal of targets are incoming from M different addition to sparsity, the received signals have the property of low rank. It ensures that we can use the partial data (we determine it as incomplete data)to recovery the full data(we determine it as complete data). The combination of matrix completion with compress sensing will bring the advantage of get higher resolution estimation of DOA with less array elements
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