###### Abstract  
To improve the positioning accuracy of unmanned ships, a multi-sensor system including ZigBee, a Global Positioning System (GPS), and BeiDou Navigation Satellite System (BDS) is constructed, and an adaptive multi-sensor data fusion positioning method based on the threshold and hierarchical capacity particle filter (TCPF) is designed. First, the ZigBee-GPS/BDS multi-sensor measurement data is preprocessed to achieve a consistent space-time reference and transformed into the same coordinate system by projection. Then, the fault data is weighted and corrected through the consistency inspection of ZigBee-GPS/BDS multi-sensor positioning data, and the corresponding confidence factor is given according to the confidence distance of the positioning data; furthermore, the confidence factor is associated with stratified sampling. After that, the multi-sensor positioning data is filtered and denoised using a basic particle filter. Finally, a TCPF data fusion algorithm is designed, and the navigation positioning data of the unmanned ship is fused and filtered to obtain its positioning information. Numerical tests show that compared with other filtering algorithms, the mean square root error and standard deviation of the proposed TCPF algorithm decrease by an average of 25.0% and 28.0%, respectively, which verifies its high filtering accuracy and its advantages in suppressing particle degradation and avoiding sample scarcity. The experimental tests show that compared with other fusion algorithms, the proposed TCPF algorithm can not only realize the precise positioning during unmanned ship navigation, but also in the positioning and fault tolerance test, the average positioning error, root-mean-square error, and standard deviation of the former decrease by 36.0%, 38.0%, and 37.0%, respectively, and the corresponding performance indicators of the latter decrease by an average of 20.0%, 19.5%, and 17.5%, which verifies that it has the advantages of high data reliability and good filtering fault tolerance, and helps to improve the positioning accuracy of unmanned ships. unmanned ship; multi-sensor data; data fusion; positioning system; particle filter +
Footnote †: journal: Applied
turbidity, dissolved oxygen, etc., and can autonomously conduct regular water quality tests throughout the river area and transmit real-time data to a monitoring center via the Internet. This method has the following advantages: it (1) reduces labor costs; (2) improves detection efficiency; (3) unaffected by subjective factors, improves accuracy; and (4) ensures timely data availability. From the above analysis, it can be seen that unmanned ships play a key role in modern water quality monitoring, with their autonomous navigation positioning accuracy directly affecting safety and efficiency. How to achieve accurate positioning of unmanned ships based on multiple combined sensors has been the focus of research [2]. [PERSON] et al. [3] used GNSS/GPS combination sensors to obtain coordinates of unmanned ship positions while reducing noise interference caused by long-distance nodes, but they suffer from signal loss due to external environmental interference leading to reduced positioning accuracy. [PERSON] et al. [4] realized the positioning of the unmanned ship by solving its position information collected by the GPS/INS combined sensors, but the INS has the problem of mechanical fault jumping. As time goes by, the GPS/INS combined sensor is likely to cause a large positioning deviation of the unmanned ship. [PERSON] et al. [5] obtained the position information of the unmanned ship by fusing the GPS/IMU combined sensor data, but in the fusion process, if the sensor data is lost at a certain moment, it will cause a large fusion deviation and affect the positioning accuracy. Data fusion based on two kinds of sensors is the current mainstream technology for unmanned ship positioning, but there is a deficiency in that the positioning accuracy and reliability are affected due to the weakening or loss of a certain sensor signal.