TY - GEN
T1 - Situation assessment model for UAV disaster relief in the city
AU - Ren, Jia
AU - Gao, Xiao Guang
PY - 2011
Y1 - 2011
N2 - A situation assessment model based on structure-variable discrete dynamic Bayesian network (SVDDBN) of sorting information is proposed for Unmanned Aerial Vehicle (UAV), which can be applied in the condition of disaster relief in the city when pop-up threats appear. The model is built on the basis of the SVDDBN, makes an uncertain classification on popup threats observing information with the help of the posterior probability support vector machine (PPSVM), and finally inputs the classification results into the assessment model as the evidence. For the features of the multi-hidden nodes of the assessment model, the forward algorithm is introduced into the probability inference of the network model, and the inference algorithm of the SVDDBN under the multi-nodes is worked out. The situation that the UAV detects the pop-up threats in the air while conducting the disaster relief in the city is set as the background to verify the correctness of the establishment of the model and the related algorithm.
AB - A situation assessment model based on structure-variable discrete dynamic Bayesian network (SVDDBN) of sorting information is proposed for Unmanned Aerial Vehicle (UAV), which can be applied in the condition of disaster relief in the city when pop-up threats appear. The model is built on the basis of the SVDDBN, makes an uncertain classification on popup threats observing information with the help of the posterior probability support vector machine (PPSVM), and finally inputs the classification results into the assessment model as the evidence. For the features of the multi-hidden nodes of the assessment model, the forward algorithm is introduced into the probability inference of the network model, and the inference algorithm of the SVDDBN under the multi-nodes is worked out. The situation that the UAV detects the pop-up threats in the air while conducting the disaster relief in the city is set as the background to verify the correctness of the establishment of the model and the related algorithm.
KW - Posterior probability support vector machine
KW - Situation assessment
KW - Structure-variable discrete dynamic bayesian network
KW - Unmanned aerial vehicle
UR - http://www.scopus.com/inward/record.url?scp=79951740400&partnerID=8YFLogxK
U2 - 10.1109/M2RSM.2011.5697400
DO - 10.1109/M2RSM.2011.5697400
M3 - 会议稿件
AN - SCOPUS:79951740400
SN - 9781424494040
T3 - 2011 International Workshop on Multi-Platform/Multi-Sensor Remote Sensing and Mapping, M2RSM 2011
BT - 2011 International Workshop on Multi-Platform/Multi-Sensor Remote Sensing and Mapping, M2RSM 2011
T2 - 2011 International Workshop on Multi-Platform/Multi-Sensor Remote Sensing and Mapping, M2RSM 2011
Y2 - 10 January 2011 through 12 January 2011
ER -
