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Metasurface antennas have been proposed for computational imaging (CI) systems, which can reconstruct images without using mechanical scanning or large antenna arrays. In a CI system based on metasurface antennas, a variety of different radiation fields, which can be applied to sample the objects, are generated by exciting different frequency points in broadband. According to the compressed sensing theory, the imaging performance of the system is mainly limited by frequency-diversity radiation modes. In general, it is difficult to achieve rich radiation modes; therefore, a special design of metasurface aperture is required. In this paper, we propose a frequency-diversity MIMO metasurface antenna that consists of 2 × 2 sub-apertures with randomly distributed surface impedance. By employing the aperture rotation technique (ART) which rotates the MIMO metasurface antenna around the panel axis, the pseudo-randomness of the radiation fields is utilized. The diversity of the radiation field is improved on the premise of ensuring the relatively low complexity of the system. The ART significantly improves the measurement richness at the cost of increasing the measurement time. The performance of the proposed method is evaluated through simulations and experiments, suggesting that the proposed 2 × 2 MIMO metasurface antenna and the ART are effective to reconstruct high-quality images.

This article presents a holographic metasurface antenna with stochastically distributed surface impedance, which produces randomly frequency-diverse radiation patterns. Low mutual coherence electric field patterns generated by the holographic metasurface antenna can cover the K-band from 18 to 26 GHz with 0.1 GHz intervals. By utilizing the frequency-diverse holographic metasurface (FDHM) antenna, we build a near-field microwave computational imaging system based on reflected signals in the frequency domain. A standard horn antenna is adopted to acquire frequency domain signals radiated from the proposed FDHM antenna. A detail imaging restoration process is presented, and the desired targets are correctly reconstructed using the 81 frequency-diverse patterns through full-wave simulation studies. Compressed sensing technique and iterative shrinkage/thresholding algorithms are applied for the imaging reconstruction. The achieved compressive ratio of this computational imaging system on the physical layer is 30:1.