Near-Field Phase Cross Correlation Focusing Imaging and Parameter Estimation for Penetrating Radar

Penetrating radar systems are widely used to image the objects that are buried inside mediums (such as walls, ground, and so on). However, due to the phase error caused by the refraction of mediums, the object images obtained by directly applying the imaging methods that ignore the existence of the mediums are defocused, which affects the recognition of small objects. Conventional focus imaging algorithms typically obtain a focused image by iteratively searching for optimal compensation, which is very time-consuming and can result in overfocusing. To solve this problem, a near-field phase cross correlation (PCC) focusing imaging algorithm is proposed in this article. First, a free-space imaging model is established to replace the unknown half-space (air-to-medium) imaging model. Then, by analyzing the relationship between the free-space and the unknown half-space imaging models, the phase error and a reference depth in free-space imaging model are determined. The focused image can then be obtained by compensating for the phase error, which is directly estimated by the proposed PCC method. The permittivity and object depth can then be estimated based on the slope of the phase error and the reference depth. Furthermore, the proposed algorithm can be extended to the multi-layered model in a straightforward manner. Extensive simulations and experiments are presented to validate the proposed methods. The results show that the proposed PCC algorithm can effectively compensate the phase error and obtain high-quality images, and the permittivity and object depth can be accurately estimated in single-layer medium cases.