###### Abstract
In recent years, how to improve positioning accuracy by further fusing data from three or more types of sensors has become a research hotspot. [PERSON] et al. [6] achieved the reliable positioning of trains by using the combined positioning method of PPP-GPS/IMU. However, the built multi-sensor positioning system is prone to environmental interference, which reduces the reliability of data samples. [PERSON] et al. [7] realized the elimination of abnormal signals by setting outlier identification in the process of IMU/ODM/UWB multi-sensor data fusion, which improved the positioning accuracy of multi-sensors, but reduced the fault-tolerant performance of the algorithm because the fault data was not weighted. [PERSON] et al. [8] divided the observation information of different sensors into blocks according to the size of the scale to obtain a multi-scale system model, which effectively solved the problem of measurement delay, but the efficiency and accuracy of algorithm fusion were reduced because no data confidence assignment was performed during data processing. As can be seen from the above, adding sensors helps to improve the reliability and accuracy of positioning. However, there is still a lack of research on how to further enhance the accuracy and efficiency of algorithm fusion through data confidence interval testing, as well as how to achieve effective positioning under low- or weak-signal conditions through data correction. Therefore, a ZigBee-GPS/BDS multi-sensor positioning system is built in this paper, which realizes the accurate and reliable positioning of unmanned ships by complementing and weighted-correcting signals from three types of sensors, as well as assigning confidence values. In multi-sensor data fusion, filtering algorithms are crucial, and particle filtering has been widely used due to its ability to handle various probability distribution models, but it has also become the focus of research due to its heavy dependence on initial state estimation and particle degradation. [PERSON] et al. [9] expanded the search space of particle states by designing crossover operators and mutation operators in the particle filter calculation module, which improved the filtering accuracy of multi-sensor data but also reduced the computational efficiency. [PERSON] et al. [10] processed the prefetched value in the weight storage of the particle-filter algorithm in parallel with the value in the random function generator, which reduced the time required for resampling and improved the fusion efficiency of sensor data, but it did not suppress the degradation of particle samples very well. [PERSON] et al. [11] used the unscented particle-filter algorithm with constrained residuals to fuse the two sets of sensor positioning data, which effectively overcomes external environmental disturbances. However, the filtering accuracy is prone to divergence after multiple iterations. A large number of studies have shown that although the existing multi-sensor data fusion algorithms have improved the positioning accuracy of moving targets to a certain extent, their fault-tolerant performance is not good. In addition, they did not carry out pre-judgment processing on the collected data, and the problem of low filtering accuracy has not been solved when the data collected by the multi-sensor combined positioning system is fused and filtered. Therefore, a multi-sensor data fusion positioning method based on adaptive threshold and hierarchical capacity particle filter is designed in this paper. Firstly, the basic particle filter is introduced to denoise multi-sensor data. Then, the latest multi-sensor positioning data is incorporated into the proposal distribution using unscented transformation. After that, the Gaussian mixture model is constructed and the adaptive threshold is set, and the data confidence factor is associated with hierarchical sampling to further improve the fusion filtering accuracy of the algorithm.