A theoretical framework to implement multi-sensor data fusion methods for kinematic quantities is proposed. All defined through the allow combination of signals obtained from position, velocity and acceleration sensors addressing same target, improvement in observation kinematics target. Differently several alternative methods, considered ones need no dynamic and/or error models operate can be ...

As intelligent driving vehicles came out of concept into people’s life, the combination safe and artificial intelligence becomes new direction future transportation development. Autonomous technology is developing based on control algorithms model recognitions. In this paper, a cloud-based interconnected multi-sensor fusion autonomous vehicle system proposed that uses deep learning (YOLOv4) imp...

This paper presents a new optimal data fusion methodology based on the adaptive fading unscented Kalman filter for multi-sensor nonlinear stochastic systems. This methodology has a two-level fusion structure: at the bottom level, an adaptive fading unscented Kalman filter based on the Mahalanobis distance is developed and serves as local filters to improve the adaptability and robustness of loc...

Data fusion involves multi-sources or multi-presentations of a single source to perform inferences which are more comprehensive and accurate than those of any single method. Thus, data fusion makes it possible to create a synergistic process in which the consolidation of individual data creates a combined resource with a productive value greater than the sum of its parts. While considerable res...

Characteristics of reflection spectrum, multi-spectral images and temperature of lettuce canopy were gained to judge the lettuce’s water stress condition which could lead to a precise, rapid & stable test of lettuce moisture and enlarged the models’ universality. By the extraction of lettuce’s multisensor characteristics in 4 different levels, quantitative analysis model of spectrum including 4...