Application of Kirchhoff migration principle for hardware-efficient near-field radar imaging

Achieving high imaging resolution in conventional monostatic radar imaging with mechanical scanning requires excessive acquisition time. Although real aperture radar systems might not suffer from such a limitation in acquisition time, they may still face challenges in achieving high imaging resolution, especially in near-field (NF) scenarios, due to diffraction-limited performance. Even with sophisticated electronic scanning techniques, increasing the aperture size to improve resolution can lead to complex hardware setups and may not always be feasible in certain practical scenarios. Although multistatic systems provide flexibility in increasing the effective aperture virtually, they introduce challenges related to the number of antennas and channels, which can still pose fundamental hardware limitations. This is because array antennas can be expensive, exhibit a poor form-factor, and typically consume a lot of power since they require complex control circuitry and many radio frequency components. An alternative solution that has been proposed in recent years is the compression of the physical layer using metasurface transducers. In this paper, by leveraging dynamic metasurface antennas, with multiple tuning states called masks, in a bistatic structure, a novel approach for NF radar imaging using the Kirchhoff migration principle is presented. By expanding the compressed measured signal using a data transformation from the mask-frequency domain to the spatial-frequency domain, the scene's spatial content is decoded. The Kirchhoff integral is then developed based on the introduced special imaging structure to retrieve the three-dimensional spatial information of the target. By using numerical simulations, a comprehensive analysis of the characteristics of masks and their behavior under different conditions is provided. Also, the performance of the image reconstruction algorithm is evaluated in terms of visual quality and computing time using both central processing unit and graphics processing unit. The results of computer simulations confirm the high reliability of the proposed approach in various cases.