## 8 Numerical Simulation and Experimental Testing
### Experimental Testing and Analysis
#### 8.2.2 Unmanned-Ship Positioning Test Based on Multi-Sensor Data Fusion  
In order to verify the superior performance of the proposed TCPF algorithm, some unmanned-ship navigation and positioning experiments based on the multi-sensor positioning system were carried out. According to the unmanned-ship positioning data collected by the sensors in the A/B experimental river, the test results of the six algorithms, namely TCPF, EKF, UKF, UPF, KF, and BPF, were compared against the three performance indicators of average positioning error, RMSE and Std. The filtering data of the proposed TCPF algorithm was ZigBee-GPS/BDS positioning data, and the sensor positioning data of the other five algorithms were all ZigBee-GPS. Figure 7 shows the filtering results of the unmanned-ship navigation trajectory based on six filtering algorithms. Figure 5: Experimental environment for unmanned ship positioning: (**a**) Communication node; (**b**) PC terminal. Figure 6: Schematic diagram of node layout and navigation trajectory on A/B river: (**a**) ZigBee node layout; (**b**) navigation trajectory of unmanned ship. From Figure 7b, it can be seen that when the unmanned ship soiled on the experimental river in section B, the sensor signal transmission was affected to a certain extent by environmental disturbances, such as dense trees and a narrow river. Except for the TCPF algorithm, the filtering and positioning results of other algorithms seriously deviated from the expected trajectory of the unmanned ship in some places; in particular, at the turning of the river, the positioning results were prone to large jumps, which caused the unmanned ship to gradually deviate from the expected trajectory and reduced the positioning accuracy and reliability. From Figure 7a, it can be seen that because the water surface of the experimental river in section A was more open than that in section B, and there were fewer shelters along the river, the positioning results of the other five algorithms showed that the unmanned ship could basically navigate according to the expected trajectory, but there was still a certain jump at the turning. Combining Figure 7a,b, it can be seen that compared with the other five algorithms, the positioning accuracy of the unmanned ship based on the proposed TCPF algorithm was significantly higher. No matter whether at the turning of the river or in a straight line, the unmanned ship could basically navigate along the expected trajectory. This was mainly because the TCPF data fusion algorithm achieves the weighted correction of multi-sensor fault data through consistency inspection, which improves the fault-tolerant performance of the algorithm, and makes the TCPF algorithm have a high-reliability data sample for filtering. If the data are not inspected and corrected, when a type of sensor is subjected to a large environmental disturbance, the positioning data will be deviated, which will reduce the credibility of the data sample, and then reduce the positioning accuracy of the unmanned ship. Thus, this shows that the proposed TCPF algorithm of ZigBee-GPS/BDS not only ensures the accuracy of fault data inspection but also improves the fault-tolerant performance of the algorithm. Tables 2 and 3 show the comparison results of trajectory positioning for six algorithms. From the tables, it can be seen that the test results of the proposed TCPF algorithm were smaller than those of the other five algorithms, regardless of the average positioning error or the maximum positioning error of the A/B section, which indicates that the proposed method has the highest filtering and fusion accuracy. This is mainly because the posterior probability density function representing the state was approximated as a Gaussian mixture distribution in this paper, and particle samples were extracted on it instead of resampling, which ensured the diversity of ZigBee-GPS/BDS multi-sensor sampling particles, and avoided a lack of samples.