Multisensor Fusion and Integration: Theories, Applications, and its Perspectives

Luo, R.C.; Chang, Chih Chia; Lai, Chun-Chi

doi:10.1109/jsen.2011.2166383

Cited by 119 publications

(52 citation statements)

References 63 publications

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“…The sensor data can be combined at the data level, the feature level and the decision level [14,23]. It requires interdisciplinary knowledge and techniques drawn from digital signal processing, statistical estimation and probability, control theory and artificial intelligence [14,28]. It has widespread applications including military applications, e.g.…”

Section: Sensor Fusionmentioning

confidence: 99%

Position stabilisation and lag reduction with Gaussian processes in sensor fusion system for user performance improvement

Feng

Murray-Smith

Ramsay

2016

Int. J. Mach. Learn. & Cyber.

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In this paper we present a novel Gaussian Process (GP) prior model-based sensor fusion approach to dealing with position uncertainty and lag in a system composed of an external position sensing device (Kinect) and inertial sensors embedded in a mobile device for user performance improvement. To test the approach, we conducted two experiments: (1) GPs sensor fusion simulation. Experimental results show that the novel GP sensor fusion helps improve the accuracy of position estimation, and reduce the lag (0.11 s). (2) User study on a trajectory-based target acquisition task in a spatially aware display application. We implemented the real-time sensor fusion system by augmenting the Kinect with a Nokia N9. In the trajectory-based interaction experiment, each user performed target selection tasks following a trajectory in (a) the Kinect system and (b) the sensor fusion system. In comparison with the Kinect time-delay system, our system enables the user to perform the task easier and faster. The MSE of target selection was reduced by 38.3 % and the average task completion time was reduced by 26.7 %.

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Section: Sensor Fusionmentioning

confidence: 99%

Position stabilisation and lag reduction with Gaussian processes in sensor fusion system for user performance improvement

Feng

Murray-Smith

Ramsay

2016

Int. J. Mach. Learn. & Cyber.

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“…In [32], the main architectures are listed for sensor data fusion. The overall goal is to maximize the accuracy and minimize the complexity of the navigation system.…”

Section: Multisensormentioning

confidence: 99%

“…The level of integration can be defined by the variables used in the integration. Deeper levels of integration operate in range and tracking domains while more loose integration on the position domain [26,27,32,33,34,35,36].…”

Section: Multisensormentioning

confidence: 99%

Overview of positioning technologies from fitness-to-purpose point of view

Basiri

Lohan

Silva

et al. 2014

International Conference on Localization and GNSS 2014 (ICL-GNSS 2014)

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Abstract-Even though Location Based Services (LBSs) are being more and more widely-used and this shows a promising future, there are still many challenges to deal with, such as privacy, reliability, accuracy, cost of service, power consumption and availability. There is still no single low-cost positioning technology which provides position of its users seamlessly indoors and outdoors with an acceptable level of accuracy and low power consumption. For this reason, fitness of positioning service to the purpose of LBS application is an important parameter to be considered when choosing the most suitable positioning technology for an LBS. This should be done for any LBS application, since each application may need different requirements. Some location-based applications, such as locationbased advertisements or Location-Based Social Networking (LBSN), do not need very accurate positioning input data, while for some others, e.g. navigation and tracking services, highlyaccurate positioning is essential. This paper evaluates different positioning technologies from fitness-to-purpose point of view for two different applications, public transport information and family/friend tracking.

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“…SRO′_rs is the new SRO_rs wherein the first n-ary meta-attribute columns are the meta-attributes of SRO 1 _rs, and the succeeding m-ary meta-attribute columns are the meta-attributes of SRO 2 _rs.  SRO′_rs = SRO 1 _rs ⋈ SRO 2 _rs ≡ {t | t = <t 1 , t 2 > ∧ t 1 ∈ SRO 1 _rs ∧ t 2 ∈ SRO 2 _rs ∧ t 1 [B] = t 2 [B]}, where t1 and t2 are the meta-attribute variables of SRO′_rs. SRO 1 _rs and SRO 2 _rs have the same meta-attribute column B = D = D , i.e., B is the common meta-attribute of these two SRO_rs instances and denotes the natural join operation derived from the product operation.…”

Section: Remote Sensor Resource Operationsmentioning

confidence: 99%

An Object Model for Integrating Diverse Remote Sensing Satellite Sensors: A Case Study of Union Operation

Chen

et al. 2014

Remote Sensing

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Abstract:In the Earth Observation sensor web environment, the rapid, accurate, and unified discovery of diverse remote sensing satellite sensors, and their association to yield an integrated solution for a comprehensive response to specific emergency tasks pose considerable challenges. In this study, we propose a remote sensing satellite sensor object model, based on the object-oriented paradigm and the Open Geospatial Consortium Sensor Model Language. The proposed model comprises a set of sensor resource objects. Each object consists of identification, state of resource attribute, and resource method. We implement the proposed attribute state description by applying it to different remote sensors. A real application, involving the observation of floods at the Yangtze River in China, is undertaken. Results indicate that the sensor inquirer can accurately discover qualified satellite sensors in an accurate and unified manner. By implementing the proposed union operation among the retrieved sensors, the inquirer can further determine how the selected sensors can collaboratively complete a specific observation requirement. Therefore, the proposed model provides a reliable foundation for sharing and integrating multiple remote sensing satellite sensors and their observations.

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Multisensor Fusion and Integration: Theories, Applications, and its Perspectives

Cited by 119 publications

References 63 publications

Position stabilisation and lag reduction with Gaussian processes in sensor fusion system for user performance improvement

Position stabilisation and lag reduction with Gaussian processes in sensor fusion system for user performance improvement

Overview of positioning technologies from fitness-to-purpose point of view

An Object Model for Integrating Diverse Remote Sensing Satellite Sensors: A Case Study of Union Operation

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