Microwave computational imaging in frequency domain with reprogrammable metasurface

Recently, microwave computational imaging systems have had various applications ranging from security screening to biomedical diagnosis. However, existing methods are sensitive to noise and have a heavy computational burden in a three-dimensional (3-D) imaging scene. A computational imaging method approached in frequency domain is proposed, which improves imaging quality under noisy conditions and reduces computation complexity. The signal-to-noise ratio of the echo signal is improved by dechirping pulse compression method, which obtains the range resolution concurrently. According to the information of range resolution, the scene is divided into some range bins. With computational imaging algorithms, the azimuth and elevation resolution are obtained in each range bin by spatially diverse patterns of reprogrammable metasurface. A sparse 3-D image can be obtained by combining the reconstructed subimages. The simulation result shows that the proposed method outperforms the conventional methods with better antinoise ability and lower computational complexity in sparse 3-D scene imaging.