# High-Resolution Through-the-Wall Radar Imaging with Exploitation of Target Structure
## 5 Conclusions  
This paper focused on the problem of multipath exploitation for TWRI in view of sparsity-based reconstruction methods. The underlying idea is to take advantage of both the group sparsity due to the multipath propagations and the continuous clustered structure on the targets. A novel CS-based multipath exploitation clustered BCS approach for image reconstruction was proposed. Benefiting from the data-driven capability of the nonparametric hierarchical Bayesian model, the proposed method achieved high-resolution TWRI without any prior information about the targets, such as shapes, sizes, and numbers. In addition, it is worth mentioning that the proposed algorithm is not restricted to the ULA application, and can be generalized to any other array shapes.  
The proposed method has the capability of acquiring high-resolution target images with free ghosts in the indoor multipath propagation environment, and thus it provides opportunities to better capture indoor messages through the wall, such as the shape of indoor stationary items, locations of indoor human targets, etc. It would also prompt further feature extraction and recognition of indoor targets behind the wall. Although the proposed method enables obtaining ghost-free high-resolution indoor targets image, the following challenges remain to be addressed in future work: (1) Prior knowledge of the number of propagation paths and their topology was assumed and provided for multipath exploitation; however, it is difficult to accurately acquire this information in practice. Therefore, it would be a challenging issue to acquire ghost-free high-resolution TWRI for multipath exploitation, when the indoor topology is inaccurate or completely unknown. (2) A centralized scheme with a uniform linear array was used where all measurements are available at a single processing node. However, a decentralized approach would be beneficial for practical purposes. Each of the nodes carries out part of the reconstruction while communicating with the other units. This results in the simpler and more flexible operation of the TWRI system and avoids a single point of failure. Proper algorithm design should ensure that the reconstruction quality is on par with centralized processing.  
In summary, it is still quite challenging for the high-resolution TWRI in the multipath-propagation environment for practical purposes due to the diversities and complexities of the indoor configurations.  
Conceptualization, Q.W.; methodology, Q.W. and C.X.; software, Q.W.; validation, Q.W. and C.X.; formal analysis, C.X.; investigation, Q.W.; resources, Q.W.; data curation, Q.W.; writing--original draft preparation, C.X.; writing--review and editing, Q.W.; visualization, C.X.; supervision, Q.W.; funding acquisition, Q.W. All authors have read and agreed to the published version of the manuscript.  
The work was supported in part in part by Key Laboratory of Underwater Acoustic Countermeasure Technology under Grant No. 2022 JCJQLB03305, National Natural Science Foundation under Grants No. 91938203, in part by Fundamental Research Funds for the Central University  
Figure 6: Comparison of reconstruction results in the experimental dataset.  
under Grant No. 2242022k30016, in part by National Defense Basis Scientific Research program of China under Grant No. JCKY2019110C143, and in part by Science and Technology on Near-Surface Detection Laboratory Pre-research Fund under Grant No. 6142414200505.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.