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Scope
Our aim is to publish research which addresses the scientific and engineering challenges of deriving information, knowledge and its interpretation, and actionable intelligence regarding the behaviors of physical systems, from multiple sources of electronic inputs.Information fusion from multiple sources with differing modality and context is particularly important in the synthesis of autonomous, intelligent machines that are capable of producing more robust and accurate perception of a physical process than what would be possible from traditional single-source, single-sensor methods.
Our specialty welcomes papers concerned with harnessing the potential of a data-rich world through multi-sensor, multi-level, data-to-decision approaches for object classification, target tracking, distributed sensor network architectures for pattern discovery, situation awareness, machine learning, and data mining applications. We encourage submissions that advance machine intelligence beyond its current limitations and address complex societal problems incorporating contextual interpretation of emerging information, cross-sensory fusion, neural computing, single or multi-sensor robot collaboration, in diverse applications in area surveillance, data-driven systems science, condition monitoring and diagnostics, geo-spatial analyses, social networking, economy and humanities. Perceptual user interfaces that add human understandable rendering of complex data sources and facilitate human-machine interactions are also of interest. Our ultimate goal is to accelerate and promote the progress in machine perception and cognition of sensed information.
Authors submitting manuscripts other than Original Research should highlight in their cover letter how their publication record on the same topic has prepared them for contributing to the field. We also request that prospective organizers of Research Topics (info at www.frontiersin.org/about/RTGuidelines) provide the Editorial Office with their CV and clarify in the proposal how their research expertise qualifies them to organize a Research Topic on that particular subject.
Indexed in: Google Scholar, DOAJ, CrossRef, dblp, Ulrich's Periodicals Directory, ESCI, Emerging Sources Citation Index, CLOCKSS
PMCID: NA
Sensor Fusion and Machine Perception welcomes submissions of the following article types: Book Review, Code, Correction, Data Report, Editorial, General Commentary, Hypothesis and Theory, Methods, Mini Review, Opinion, Original Research, Perspective, Review and Technology Report.
All manuscripts must be submitted directly to the section Sensor Fusion and Machine Perception, where they are peer-reviewed by the Associate and Review Editors of the specialty section.
Articles published in the section Sensor Fusion and Machine Perception will benefit from the Frontiers impact and tiering system after online publication. Authors of published original research with the highest impact, as judged democratically by the readers, will be invited by the Chief Editor to write a Frontiers Focused Review - a tier-climbing article. This is referred to as "democratic tiering". The author selection is based on article impact analytics of original research published in all Frontiers specialty journals and sections. Focused Reviews are centered on the original discovery, place it into a broader context, and aim to address the wider community across all of Robotics and AI.
Frontiers Editorial Office
EPFL - Innovation Square, Building I
CH – 1015 Lausanne
Switzerland
Tel +41(0)21 510 17 40
Fax +41 (0)21 510 17 01
Frontiers Support
Tel +41(0)21 510 17 10
Fax +41 (0)21 510 17 01
support@frontiersin.org
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Contact Editorial Office
EPFL - Innovation Square, Building I
CH – 1015 Lausanne
Switzerland
Tel +41(0)21 510 17 40
Fax +41 (0)21 510 17 01
Tel +41(0)21 510 17 10
Fax +41 (0)21 510 17 01