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...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:2021 7th International Conference on Computer and Communications (ICCC) s. 288 - 293
Hlavní autori: Li, Xiguang, Zhao, Yue, Fan, Chunlong, Qiu, Xinye
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 10.12.2021
Predmet:
Computer simulation
D-S evidence theory
Data collection
Data integration
Deng entropy
Entropy
Multi-sensor data fusion
Probability distribution
Quantitative uncertainty
Uncertainty
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
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