Multi-Sensor Information FusionXue-Bo Jin, Yuan Gao This book includes papers from the section “Multisensor Information Fusion”, from Sensors between 2018 to 2019. It focuses on the latest research results of current multi-sensor fusion technologies and represents the latest research trends, including traditional information fusion technologies, estimation and filtering, and the latest research, artificial intelligence involving deep learning. |
Common terms and phrases
according accuracy algorithm analysis applied approach attack better calculated centralized combination compared complex computational considered Control correlation covariance CrossRef data fusion decision defined designed detection determined developed distance distributed dynamic effect Equation error estimation evaluation evidence example experiments expressed extraction fault Figure fire follows frame framework function fused fusion given IEEE Trans improve initial input Kalman filter layer learning linear maintenance matrix mean measurement method multi-sensor multiple noise objects observable obtained optimal output parameters performance position prediction presented probability problem Proceedings proposed quaternion random range reference represents respectively sampling segmentation selected sensor shown in Figure shows signal similarity simulation step supervoxel surface Syst Table theory tracking transformation uncertainty update variance vector vehicle weight