In this paper the multi sensor fusion results obtained within the European research project GEODE (Ground Explosive Ordnance Detection system) are presented. The lay out of the test lane and the individual sensors used are described. The implementation of the SCOOP algorithm improves the ROC curves, as the false alarm surface and the number of false alarms both are taken into account. The confi...

Basic probability assignment (BPA) has the key role in multi-sensor data fusion. This paper proposed a way to determine BPA using the various signals acquired from the sensors. It described the analysis of signals detected by the sensors. The determined BPA were used for multi-sensor data fusion to infer and recognize the context targeted by a wireless sensor networks. To determine BPA, the cha...

A novel interactive web application for multi focus and multi sensor image fusion is presented in this paper. Basic averaging, Laplacian pyramid, discrete wavelet transform, block-based fusion, spatial domain multi focus image fusion and optimized region based image fusion methods are also included. Users can explore several image fusion methods and compare these methods easily and efficiently ...

The multi-sensor fusion can provide more accurate and reliable information compared to information from each sensor separately taken. Moreover, the data from multiple heterogeneous sensors present in the medical surveillance systems have different degrees of uncertainty. Among multi-sensor data fusion techniques, Bayesian methods and evidence theories such as Dempster-Shafer Theory (DST), are c...

The multi-sensor fusion can provide more accurate and reliable information compared to information from each sensor separately taken. Moreover, the data from multiple heterogeneous sensors present in the medical surveillance systems have different degrees of uncertainty. Among multisensor data fusion techniques, Bayesian methods and evidence theories such as Dempster-Shafer Theory (DST), are co...

In real applications, how to measure the uncertain degree of sensor reports before applying sensor data fusion is a big challenge. In this paper, in the frame of Dempster-Shafer evidence theory, a weighted belief entropy based on Deng entropy is proposed to quantify the uncertainty of uncertain information. The weight of the proposed belief entropy is based on the relative scale of a propositio...

The principle of target tracking and data fusion techniques are discussed. To resolve high uncertainty that exists in sensors of mobile robots, one multi-sensor data fusion algorithm is presented. The algorithm is based on particle filter techniques, fuses the information coming from multiple sensors and merges different state space models. So it can be used to eliminate system and measurement ...

Some multi-sensor fusion systems are not asymptotic stable like target tracking system. When using classical H∞ filtering theory to design the fusion filter for these systems, there are no feasible solutions to the problem. Applying H∞ theory and LMI methods to design the H∞ fusion filter for those kind of fusion systems, in which the process and measurement noise have unknown statistic charact...

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...

A new DS (Dempster-Shafer) combination method is presented in this paper. As data detected by a single sensor are characterized by not only fuzziness, but also partial reliability, the development of multi-sensor information fusion becomes extremely indispensable. The DS evidence theory is an effective means of information fusion, which can not only deal with the uncertainty and inconsistency o...