Sparse Array Microwave 3-D Imaging: Compressed Sensing Recovery and Experimental Study

Abstract

Abstract-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.

Wide-Band Secure Compressed Spectrum Sensing for Cognitive Radio Systems

“…CS has also been successfully deployed in track-beforedetect radar systems, which reconstruct the whole radar scene from sparse measurements [12]. Also using CS, low-cost high-quality sparse array 3-D microwave imaging have recently been shown to be feasible [13].…”

3-D Sar Imaging Based RCS Measurement Technique With Fixed Transmitter

“…However, considering that the 2-D/3-D radar imaging techniques have been used more and more [16,17], the imaging based RCS measurement results are more practical and useful than the electromagnetic computing results.…”

Compressed Sensing Based Track Before Detect Algorithm for Airborne Radars

“…A great deal of compressed sensing based methods have been applied to radar systems [8][9][10][11][12][13][14][15][16][17][18], which recover the target scene from fewer measurements than traditional methods. In [8], it is demonstrated that the compressed sensing can eliminate the need for matched filter at the receiver and has the potential to reduce the required sampling rate.…”

“…[10] focuses on monostatic chaotic multiple-input-multiple-output (MIMO) radar systems and analyze theoretically and numerically the performance of sparsity-exploiting algorithms for the parameter estimation of targets at Low-SNR. In the context of synthetic aperture radar (SAR), [11][12][13][14][15][16] present compressed sensing based data acquisition and imaging algorithms. An additional sensing matrix H is introduced in [17,18], which compress the received signal further by making nonadaptive, linear projections of the direct data sampled at the Nyquist frequency.…”