Microwave array 3-D imaging is an emerging technique capable of producing a 3-D map of scattered electric fields. Its all-weather and large scene imaging features make it an attractive powerful tool for target detection and feature extraction. Typically, a microwave array 3-D imaging system based on the classical sampling theory requires a large dense 2-D antenna array, which may suffer from a very high cost. To reduce the number of the antenna array elements, this paper surveys the use of compressed sensing recovery and sparse measurement strategies for microwave array 3-D imaging. Combining with the typical spatial sparsity of the underlying scene, we pose the sparse array microwave 3-D imaging as finding sparse solutions to under-determined linear equations. Further, to reduce the computational of the compressed sensing recovery with the large-scale echoes data, we divide the underlying 3-D scene into a series of equal-range 2-D slices, and deal with these slices separately using the orthogonal matching pursuit (OMP) algorithm. Lastly, the performance of the presented compressed sensing approach is verified by an X-band microwave array 3-D imaging system. The experimental results demonstrate that the compressed sensing approach can produce a better resolution 3-D image of the observed scatterers compared with the conventional method, especially in the case of very sparse activate antenna array.
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