Multi-sensor Data Fusion Algorithm Based on Dempster-Shafer Theory

Aiming at the problem of uncertainty in multi-sensor data collection, a multi-sensor data fusion algorithm (MSDF) based on D-S theory is proposed. By calculating the distance between the data, the trust function is obtained to eliminate the abnormal data, calculate the basic probability distribution...

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Vydáno v:2021 7th International Conference on Computer and Communications (ICCC) s. 288 - 293
Hlavní autoři: Li, Xiguang, Zhao, Yue, Fan, Chunlong, Qiu, Xinye
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 10.12.2021
Témata:
Computer simulation
D-S evidence theory
Data collection
Data integration
Deng entropy
Entropy
Multi-sensor data fusion
Probability distribution
Quantitative uncertainty
Uncertainty
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Shrnutí:Aiming at the problem of uncertainty in multi-sensor data collection, a multi-sensor data fusion algorithm (MSDF) based on D-S theory is proposed. By calculating the distance between the data, the trust function is obtained to eliminate the abnormal data, calculate the basic probability distribution function for the correct data obtained as the original evidence body. The improved Deng entropy is used to quantify the uncertainty of the evidence body and form new evidence, which is fused according to the D-S theory. Computer simulation result shows that the algorithm effectively solves the high conflict problem in D-S theory and improves the accuracy of data fusion.
DOI:10.1109/ICCC54389.2021.9674478