Sparse Array Microwave 3-d Imaging: Com- Pressed Sensing Recovery and Experimental Study

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 largescale 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. Received 23 August 2012, Accepted 15 October 2012, Scheduled 18 December 2012 * Corresponding author: Shun-Jun Wei ([email protected]).