Multi-Sensor Data Fusion Method Based on Improved Evidence Theory

Abstract

To achieve autonomous navigation in complex marine environments, unmanned surface vehicles are equipped with a variety of sensors for sensing the surrounding environment and their own state. To address the issue of unsatisfactory multi-sensor information fusion in stochastic uncertain systems with unknown disturbances, an improved evidence theory multi-sensor data fusion method is proposed in this article. First, the affiliation function in fuzzy set theory is introduced as a support function to assign initial evidence for multi-sensor data, and the initial evidence is corrected according to the degree of data bias. Second, a divergence measure is employed to measure the degree of conflict and discrepancy among the evidence, and each piece of evidence is allocated proportional weight based on the conflict allocation principle. Finally, the evidence is synthesized through the evidence combination rule, and the data are weighted and summed to obtain the data fusion results. Since it is difficult to obtain dynamic information from multiple sensors carried by unmanned surface vehicles in practical applications, and considering that the proposed method has universal applicability, practical application experiments using previous research demonstrate that the proposed method has higher fusion accuracy than other existing data fusion methods.

A Fuzzy Dempster–Shafer Evidence Theory Method with Belief Divergence for Unmanned Surface Vehicle Multi-Sensor Data Fusion

“…Figures 3 and 4 exhibit the absolute and relative errors of measurement fusion for all methods. As shown in Figures 3 and 4, the fusion result of the proposed method outperforms those of the other methods at any moment, except that it is poorer than the measurement fusion results of Qiao et al [35] at the moment t 3 . At the moment t 5 , when the errors of the measurement fusion results of the other methods are relatively large, the proposed method still obtains a small measurement fusion error.…”

“…Arithmetic averaging 65.66625 0.09625 0.14678 Xiong et al [48] 65.56009 0.00991 0.01511 Qiao et al [35] 65.56785 0.00215 0.00328 Proposed method 65.56999 0.00001 0.00002…”

“…Since the accuracy information for each sensor is given in Table 7, the measurements need to be additionally fused using (11), (17), and (18). After a series of calculations, Figure 2 illustrates the measurement fusion results of the arithmetic averaging method, Xiong et al [48], the least squares method, Qiao et al [35], and the proposed method and the true value. From Figure 2, it can be observed that the proposed method is closer to the true value than the other methods, and the fluctuation in the fusion results of the other methods near the true value at different moments is larger with the proposed method, which is not conducive to the real-time fusion estimation of multiple sensors.…”

“…However, these multi-sensor data fusion methods are generally used in fields such as fault diagnosis and target identification, and they do not fuse measurements collected by multiple sensors. To solve the problem of poor accuracy of multi-sensor data fusion, an improved evidence theory multi-sensor data fusion method has been proposed to realize the fusion of measurements collected by multi-sensors by assigning weights to each piece of evidence based on the degree of support between measurements [35]. To further improve the accuracy of existing multi-sensor data fusion methods and to cope with situations where the sensor accuracy is known, in this paper, a fuzzy evidence theory multi-sensor data fusion method with belief divergence is proposed based on [35].…”

“…Arithmetic averaging 99.494 0.506 0.506 Xiong et al [48] 99.982 0.018 0.018 Qiao et al [35] 100.033 0.033 0.033 Proposed method 99.998 0.002 0.002…”