# Target Localization Based on Bistatic T/R Pair Selection in GNSS-Based Multistatic Radar System
## 5 Conclusions  
The multistatic radar system is the developmental trend of the radar research. From a different perspective, the systems composed of more than two receivers or more than two transmitters have been studied and defined as the netted radar, MIMO radar, or multistatic radar. With the increasing scale of the system, the consequent problem is the huge computational load of the signal processing. Therefore, cognitive mechanism needs to be considered to select the transmitter and receiver pair and to limit the scale of the origin datasets, but still retain the performance of the multistatic systems.  
Therefore, based on the study of the multistatic positioning, this paper proposes two multi-source selection algorithms to optimize the multistatic target localization precision. The first method, the CMF method, is a classic multistatic fusion method based on the ranging covariance matrix. The second, the CHO method, is based on the ranging vector, which is more commonly used in navigation and positioning optimization. We learn from it to evaluate the target localization capability of the multistatic radar system.  
The theoretical derivations have shown the mechanism of the two methods. To validate the theoretical model, experiments were conducted using GNSS satellites as the multiple illuminators using one receiver. More than ten satellites were seen in the experimental results, and both algorithms have good selecting capabilities and can retain the accuracy of multistatic target localization, utilizing a fewer number of satellites. When the same number of satellites are selected, the CHO method is more effective. However, in cases of larger number of transmitters or receivers, the two methods can be combined to achieve a more effective selection, for which the simulation has been conducted with 55 GNSS satellites. From the simulation results, to obtain the same level of localization precision, at least one third of the computational load can be reduced.  
The proposed methods can be extended to any multistatic radar systems, which can contribute to the practical real-time multistatic radar applications.  
Figure 15: Schematic diagram of Global Navigation Satellite System (GNSS)-based multistatic radar signal processing.  
**Author Contributions:** All authors have made significant contributions to this manuscript. Conceptualization, S.Z.; methodology, H.M.; validation, Y.S., resources, M.A.; writing--original draft preparation, Y.S.; writing--review and editing, H.M.; project administration, X.W.; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National ScienceFund for Distinguished Young Scholars (No. 61525105), the Fundfor Foreign Scholars in University Research and Teaching Programs (the 111 Project No. B18039), the program for Cheung Kong Scholars, the Postdoctoral Innovation Talent Support Program, the National Natural Science Foundation of China(No. 61901344), the National Defense Foundation of China, the foundation of Science and Technology on Electronic Information Control Laboratory, Chinese Postdoctoral Science Funding and the National Natural Science Foundation of Shaanxi Province (No. 2019Q-289). (Corresponding author: [PERSON].)  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data sharing is not applicable to this article, because the raw/processed data required to reproduce these findings also forms part of ongoing study.  
**Acknowledgments:** Special thanks to [PERSON] for his guidance on this paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.