Introduction to Multisensor Data Fusion.

TL;DR: This paper summarizes the state of the data fusion field and describes the most relevant studies, enumerate and explain different classification schemes for data fusion, and reviews the most common algorithms.

TL;DR: It is argued that next-generation human-computer interaction (HCI) designs need to include the essence of emotional intelligence - the ability to recognize a user's affective states- in order to become more human-like, more effective, and more efficient.

TL;DR: Current techniques of multi-source remote sensing data fusion are reviewed and their future trends and challenges are discussed through the concept of hierarchical classification, i.e., pixel/data level, feature level and decision level.

TL;DR: An unsupervised and incremental learning approach to the extraction of maritime movement patterns is presented here to convert from raw data to information supporting decisions, and is a basis for automatically detecting anomalies and projecting current trajectories and patterns into the future.

TL;DR: This invaluable reference shows how to implement a data fusion system, describes the process for algorithm selection, functional architectures and requirements for ancillary software, and illustrates man-machine interface requirements an database issues.

TL;DR: A description of the transferable belief model, which is used to quantify degrees of belief based on belief functions, is given and a set of axioms justifying Dempster's rule for the combination of belief functions induced by two distinct evidences is presented.

TL;DR: In this paper, a discussion is directed to least-squares estimation theory, from its inception by Gauss to its modern form, as developed by Kalman, and it is enlightening to consider just how far (or how little) we have advanced since the initial developments and to recognize the truth in the saying that we stand on the shoulders of giants.