1. Sparse direction of arrival estimation of co-prime MIMO radar using sparse aperture completion
- Author
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Zhang Jingya, Gong Zhang, and Tao Yu
- Subjects
co-prime receive arrays ,virtual receive aperture ,Computer science ,Aperture ,Acoustics ,sparse direction of arrival estimation ,Energy Engineering and Power Technology ,accurate doa estimation ,compressive sensing multiple-input and multiple-output radars ,space–time compressed signal ,output data ,co-prime mimo radar ,match filters ,cs-based difference co-array methods ,array signal processing ,space–time recovery scheme ,matched filters ,compressed sensing ,Coprime integers ,sparse aperture completion scheme ,Radar signal processing ,Matched filter ,General Engineering ,Direction of arrival ,Mimo radar ,matrix algebra ,radar signal processing ,Compressed sensing ,direction-of-arrival estimation ,Matrix algebra ,lcsh:TA1-2040 ,virtual uniform linear array ,mimo radar ,sparse target scene ,lcsh:Engineering (General). Civil engineering (General) ,structured measurement matrices ,Software - Abstract
In this study, the authors consider the problem of direction of arrival (DOA) estimation in compressive sensing multiple-input and multiple-output (CS-MIMO) radars with co-prime receive arrays. A sparse aperture completion scheme is proposed to fill the ‘holes’ that in the difference co-array, achieving the full virtual receive aperture. Structured measurement matrices are devised in order that the output data of match filters can be seen as space–time compressed signal of a virtual uniform linear array. By employing the space–time recovery scheme, the sparse target scene can be accurately recovered while the amount of samples is further reduced. Numerical results demonstrate that the proposed scheme can achieve accurate DOA estimation with space–time compressed data and outperform the CS-based difference co-array methods.
- Published
- 2019
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