Uncertainty-Constrained Belief Propagation for Cooperative Target Tracking

Xu, Chunjing; Shi, Yuchen; Wan, Jiawang; Duan, Shihong

doi:10.1109/jiot.2022.3165818

Cited by 7 publications

(3 citation statements)

References 31 publications

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“…Table 7 lists the measurements of the six sensors at seven sampling moments and the measurement accuracy of each sensor with a reference true value of 50. 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.…”

Section: Methodsmentioning

confidence: 99%

“…To obtain more reliable measurements and higher fusion accuracy, numerous multisensor data fusion methods [11][12][13] suitable for different situations have been proposed. The adaptive weighted fusion method is a relatively simple method that assigns an optimal weighting factor to each sensor according to the principle of mean square error minimization [14].…”

Section: Introductionmentioning

confidence: 99%

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

Qiao

Song

Fan

et al. 2023

JMSE

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The safe navigation of unmanned surface vehicles in the marine environment requires multi-sensor collaborative perception, and multi-sensor data fusion technology is a prerequisite for realizing the collaborative perception of different sensors. To address the problem of poor fusion accuracy for existing multi-sensor fusion methods without prior knowledge, a fuzzy evidence theory multi-sensor data fusion method with belief divergence is proposed in this paper. First of all, an adjustable distance for measuring discrepancies between measurements is devised to evaluate the degree of measurement closeness to the true value, which improves the adaptability of the method to different classes of sensor data. Furthermore, an adaptive multi-sensor measurement fusion strategy is designed for the case where the sensor accuracy is known in advance. Secondly, the affiliation function of the fuzzy theory is introduced into the evidence theory approach to assign initial evidence of measurements in terms of defining the degree of fuzzy support between measurements, which improves the fusion accuracy of the method. Finally, the belief Jensen–Shannon divergence and the Rényi divergence are combined for measuring the conflict between the evidence pieces to obtain the credibility degree as the reliability of the evidence, which solves the problem of high conflict between evidence pieces. Three examples of multi-sensor data fusion in different domains are employed to validate the adaptability of the proposed method to different kinds of multi-sensors. The maximum relative error of the proposed method for multiple sensor experiments is greater than or equal to 0.18%, and its error accuracy is much higher than the best result of 0.46% among other comparative methods. The experimental results verify that the proposed data fusion method is more accurate than other existing methods.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Qiao

Song

Fan

et al. 2023

JMSE

View full text Add to dashboard Cite

show abstract

“…To address the problem that the limitation of an unknown distribution of multi-source data leads to the poor stability of simple wavelet neural networks in a multi-source discrete data environment, Yang et al [15] proposed a decision-fusion method combining Bayesian inference and wavelet neural networks. Other data fusion methods include genetic algorithms [16], particle filtering algorithms [17], the Kalman filtering algorithm [18,19], etc. However, these methods are not capable of resolving uncertain information.…”

Section: Introductionmentioning

confidence: 99%

Multi-Sensor Data Fusion Method Based on Improved Evidence Theory

Qiao

Fan

Wang

et al. 2023

JMSE

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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.

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A survey of indoor positioning systems based on a six-layer model

Sartayeva,

Chan,

et al. 2023

Computer Networks

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Uncertainty-Constrained Belief Propagation for Cooperative Target Tracking

Cited by 7 publications

References 31 publications

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

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

Multi-Sensor Data Fusion Method Based on Improved Evidence Theory

A survey of indoor positioning systems based on a six-layer model

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