## 10 Conclusions and Future Work  
In this paper, a multi-sensor data fusion positioning method for unmanned ships based on a threshold- and hierarchical-capacity particle filter was proposed to address the issues of significant deviation in data fusion accuracy caused by signal weakening and loss in conventional integrated positioning systems in unmanned ships. Through algorithm simulation and experimental testing, the following conclusions could be obtained:  
1. The positioning data collected by the ZigBee-GPS/BDS multi-sensor is used to complement the information, which effectively overcomes the problem of sensor signal weakening or loss caused by environmental masking, and improves the accuracy and effectiveness of multi-sensor data fusion.
2. By conducting consistency checks on the multi-sensor positioning data and weighted correction of the faulty data, not only does it enhance the fault-tolerance performance of the data fusion algorithm but also strengthens the credibility of the data set samples.
3. The latest positioning data of multiple sensors are integrated into the proposal distribution of the particle set, which causes the suggested distribution to be closer to the true posterior probability density and improves the estimation performance of the algorithm. At the same time, the adaptive threshold is constructed in the cluster analysis of Gaussian mixing units, and the discrete particle samples are merged into similar component units, which improves the real-time performance and computing efficiency of the system's signal processing.
4. Through stratified sampling and the setting of proportional capacity, a sufficient number of particles in the disadvantage layer are ensured for weight optimization and combination, and the diversity of particles is improved. Simultaneously, associating confidence factors with the TCPF algorithm's layered sampling prioritizes the selection of positioning data with larger confidence factors for sample fusion during the fusion filtering process of the unmanned ships position information to enhance the efficiency and accuracy of the data fusion algorithm.  
The multi-sensor data fusion method in this paper was mainly oriented to unmanned ships that conduct water quality detection in inland rivers. In recent years, unmanned ships have also been applied in various fields such as maritime rescue, maritime monitoring, and maritime cargo transportation. The marine environment is much more complex than inland rivers. Whether the multi-sensor fusion algorithm proposed in this paper is suitable for the marine environment will be the next research topic.  
Methodology, Y.S. and Z.Z.; validation, Z.Z. and M.Y.; original draft preparation, Y.S. and Z.Z.; writing, Z.Z., M.Y. and S.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the 2022 Research Plan for Innovative Products in the Industrial Chain of Zhangjiagang City (ZKC2206) and the 2022 Industry-University Research Pre-research Fund Project of Zhangjiagang City (ZKYY2253).  
Not applicable.  
The authors would like to thank [PERSON] of the T-SEA Marine Technology Co., Ltd. for his help in the sampling and laboratory analysis.  
The authors declare no conflict of interest.