Study On Clutter Suppression And Target Detection Techniques For Through-the-wall Imaging Radar

Through-the-wall imaging radar(TWIR) can obtain two-dimensional high resolution imagery of the objects through obstacles. Therefore, TWIR is becoming an emerging technology that has a variety of potential applications including earthquake rescue, antiterrorism and reconnaissance.Target imaging with high resolution is an important indicator of TWIR system performance. However, the existence of wall, which is often non-homogenous and/or non-uniform, results in heavy clutter that makes target image invisible. Consequently, one of the key technologies in TWIR is using clutter suppression and target detection techniques to not only achieve a high-quality image but also offer valuable information for target recognition or discrimination.Based on the detection principle of TWIR, this thesis establishes electromagnetic medium penetration transmission model, and analyzes wall and target reflections in detail. Moreover, the effect of wall reflections on through-the-wall radar imaging is discussed. On this basis, statistical techniques are applied to clutter suppression, and the relationship among them is discussed theoretically. For the problem of choosing subspace, a new evaluation criterion without any priori knowledge of wall or target is proposed, which can efficiently extract target information and mitigate the clutter. Through further analysis of echo, the spatial signatures of wall and target are derived according to the configuration of multiple input multiple output(MIMO) array and wall characteristic. The results based on parametric models and finite-difference time-domain(FDTD) models demonstrate the significant differences between them. Therefore, a wall clutter suppression method based on spatial signature is proposed, which can reduce wall clutter without affecting target reflections. Furthermore, in some cases, the remnant clutter would obscure the target echo, even though the heavy clutter has been suppressed. Since human targets have drawn much attention in TWIR, a detection method based on multi-frame image is proposed. Considering the moving condition of human body, such as velocity, weighted direction is used to reduce the computational complexity and improve detection performance, which has applicable value in practice.