&lt;title&gt;Active perception in a sensor fusion model&lt;/title&gt;

Biel, Lena; Wide, Peter

doi:10.1117/12.458381

Cited by 3 publications

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“…Perception according to the model in [3] can be considered as active or passive. In this example, a process were the sensor system is only sensing and communicating relevant information, is here considered to work in a passive perception mode, i.e.…”

Section: A Proposed Modelmentioning

confidence: 99%

Human-Based Sensing – Sensor Systems to Complement Human Perception

Wide

2008

International Journal on Smart Sensing and Intelligent Systems

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The approach of human-based sensing is based on the assumption that sensor systems for individual use have optimal performance if coherent with the human perception system. The benefits of complementing the human sensing with sensor systems that strengthen the information and make the human able to perform more adequate and optimized decisions based on the sensor information, will improve the human capability. The arguments for designing sensor systems that increase the human ability in order to enhance the information from its surroundings are obvious. In this paper, an example of this approach, related to human-based sensing, will be demonstrated. The ability to strengthen the human capability, and complementing human perception with additional sensing, by the use of artificial sensor systems, will increase the human performance.

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Section: A Proposed Modelmentioning

confidence: 99%

Human-Based Sensing – Sensor Systems to Complement Human Perception

Wide

2008

International Journal on Smart Sensing and Intelligent Systems

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An intelligent model approach for combination of sensor information

Biel

Wide

IEEE International Workshop HAVE Haptic Virtual Environments and Their

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Humans gather information from the environnient around them by using different senses, e.g. sight, hearing, touch, smell and taste. By combining the sensory information, we are able to structure the decisions and actions when interacting ivitli the environment. lfunians are capable to actively use the perception capabilities in order to perform the objectives in time and space.The objective of this paper is to discuss a biologically inspii.ed sensor fusioti model, nanied Sensor Fusion Model with Active Perception (SeFh4AP). Here the biological inspiration concept is not used to indicate the biological plausibiliv bi the sense of circuitty, networking architecture and itformation exchange modalities of proposed models, but the niodeling point of view. SeFAUP has been developed by mimicking the human way of processing information received from the sensory organs. This gives a siniple and general model with great development potential, properties that in some degree are missing in the existing models. SeFAfAP was intended for modeling intelligent sensor firsioti systetns us ivell as traditional sensor fijirsion sjstetns Tlie model discussed iti this paper, SeFAJAP, includes three rnain processes (sensation perception atid active perception) as well as a knowledge base. SeFMAP rejlects the signal processing 011 the sensory irforniatiori (lint occurs on the way to the brain, as well as in the brain. The model also handles the nieriiory and the decision-making to lead tlie systetn closer to an objective that may be changed during run-time. The beneJts of SeFMAP are demonstrated in three examples, a classi$catiori application, an auditory-visual target localizatioti system and a fire indicatioti system. The paper also deals with how the time afects the result of the setisor fusion algorithm together with earlier experiences. Finally, they perform actions that lead the system closer to the objectives.In many artificial sensor fusion applications, the algorithms used in order to fuse sensor data deal only with a part of a complcte fusion system. It is therefore necessary to have the possibility to model the whole complex systcm and include thc fusion algoritlini into the design in a fcasible way. When studying the scnsor fusion literature, thc number of sensor fusion models including memory and feedback capabilitics are limited. 'L'his fact seems rcninrkablc bccausc it may causc limitations in the possibilities of modeling intelligent systems. An intelligent system in this context is a system that interacts with the cnvironnicnt, pcrccives new information and interprets it together with earlier experiences. Finally, it performs actions that lead the system closer to the objectives.The objectivc of this paper is to discuss a biologically inspircd sensor fusion model, namcd Sensor Fusion Model with Active Perception (SeFMAP). Here the biological inspiration concept is not uscd to indicate the biological plausibility in tlie scnsc of circuitry, networking architecture and information exchange modalitics of proposed models b...

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