• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.4 no.1
• /
• pp.63-69
• /
• 2004

In this paper, we propose a general framework for sensor data fusion applied to control systems. Since many kinds of disturbances are introduced to a control system, it is necessary to rely on multisensor data fusion to improve control performance in spite of the disturbances. Multisensor data fusion for a control system is considered a sequence of making decisions for a combination of sensor data to make a proper control input in uncertain conditions of disturbance effects on sensors. The proposed method is applied to a typical control system of a flexible link system in which reduction of oscillation is obtained using a photo sensor at the tip of the link. But the control performance depends heavily on the environmental light conditions. To overcome the light disturbance difficulties, an accelerometer is used in addition to the existing photo sensor. Improvement of control performance is possible by utilizing multisensor data fusion for various output responses to show the feasibility of the proposed method in this paper.

• Proceedings of the KIEE Conference
• /
• 1991.07a
• /
• pp.787-794
• /
• 1991

The objective of this paper is to survey the state of the art of multisensor data fusion in intelligent robot systems. The variety of approaches to the problem of multisensor fusion ranging from general frameworks to robotic applications is surveyed. We have classified them into three categories : sensor modeling, fusional methods, and robotic applications. Also we present research trend and future direction of multisensor fusion.

• Korean Journal of Remote Sensing
• /
• v.28 no.6
• /
• pp.611-622
• /
• 2012

This study proposed two multisensor fusion methods for segment-based image classification utilizing a region-growing segmentation. The proposed algorithms employ a Gaussian-PDF measure and an evidential measure respectively. In remote sensing application, segment-based approaches are used to extract more explicit information on spatial structure compared to pixel-based methods. Data from a single sensor may be insufficient to provide accurate description of a ground scene in image classification. Due to the redundant and complementary nature of multisensor data, a combination of information from multiple sensors can make reduce classification error rate. The Gaussian-PDF method defines a regional measure as the PDF average of pixels belonging to the region, and assigns a region into a class associated with the maximum of regional measure. The evidential fusion method uses two measures of plausibility and belief, which are derived from a mass function of the Beta distribution for the basic probability assignment of every hypothesis about region classes. The proposed methods were applied to the SPOT XS and ENVISAT data, which were acquired over Iksan area of of Korean peninsula. The experiment results showed that the segment-based method of evidential measure is greatly effective on improving the classification via multisensor fusion.

• Proceedings of the KIEE Conference
• /
• 1991.07a
• /
• pp.781-786
• /
• 1991

This paper introduces a new methodology for multisensor data fusion. The method makes use of fuzzy techniques and possibility distribution as a fuzzy restriction which acts as an elastic constraint on the values that may be assigned to a variable. We propose a simple sensor fuzzy modeling method which can be used for cluster validity analysis. As a result, the feasibility of these multisensor data fusion modules is demonstrated by computer simulation applicable to the problem of object identification.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.902-904
• /
• 2003

The main objective of this paper is to investigate the potential utility of multisensor remotely sensed data for improved coastal wetland mapping. Five data fusion models, three algebraic models (Multiplicative (MT), Brovey (BT) and Wavelet transform (WT)) and two spectral domain models (Principals component transform (PCT) and Intensity-Hue-Saturation (IHS)) were implemented and tested over the multisensor data. The fused images were then compared based on visual and statistical approaches. The results show that the wavelet transform provides greater flexibility for combining optical data sets and has good potential for preserving the spatial and spectral content of the original images . However, this model yields poor information when combining optical and microwave data. Brovey transform is more reliable for fusing optical and microwave image data and yields improved information about different wetland features of the coastal zone.

• Journal of Advanced Marine Engineering and Technology
• /
• v.24 no.1
• /
• pp.119-126
• /
• 2000

In constructing the positioning system based on a conventional dead-reckoning for a wheeled vehicle with pneumatic tires, the position estimation error is inevitable as changes of the radius of the wheels depend on live load and variable enviroment. Therefore, this paper proposes the positioning system which can estimate the error source i.e. the vehicle parameter errors, such as the right and left wheel radius error, using gyroscope and ultrasonic sensor and correct the parameter to reduce the dead-reckoned position estimation error. The extended Kalman filter was used as a method for the multisensor data fusion. The simulation to verify the effectiveness of the proposed positioning system is performed.

• Journal of the Korean Society for Precision Engineering
• /
• v.20 no.11
• /
• pp.79-90
• /
• 2003

In this paper, we propose a multisensor-based navigation algorithm for a mobile robot, which is intelligently searching the goal location in unknown dynamic environments using multi-ultrasonic sensor. Instead of using “sensor fusion” method which generates the trajectory of a robot based upon the environment model and sensory data, “command fusion” method by fuzzy inference is used to govern the robot motions. The major factors for robot navigation are represented as a cost function. Using the data of the robot states and the environment, the weight value of each factor using fuzzy inference is determined for an optimal trajectory in dynamic environments. For the evaluation of the proposed algorithm, we performed simulations in PC as well as experiments with IRL-2002. The results show that the proposed algorithm is apt to identify obstacles in unknown environments to guide the robot to the goal location safely.

• Smart Structures and Systems
• /
• v.11 no.6
• /
• pp.569-587
• /
• 2013

This paper evaluates the closed loop performance of the reaching law based discrete sliding mode controller with multisensor data fusion (MSDF) in real time, by controlling the first two vibrating modes of a piezo actuated structure. The vibration is measured using two homogeneous piezo sensors. The states estimated from sensors output are fused. Four fusion algorithms are considered, whose output is used to control the structural vibration. The controller is designed using a model identified through linear Recursive Least Square (RLS) method, based on ARX model. Improved vibration suppression is achieved with fused data as compared to single sensor. The experimental evaluation of the closed loop performance of sliding mode controller with data fusion applied to piezo actuated structure is the contribution in this work.

• 제어로봇시스템학회:학술대회논문집
• /
• 1993.10a
• /
• pp.679-684
• /
• 1993

This article presents an approach to estimate the general 3D motion of a polyhedral object using multiple, sensory data some of which may not provide sufficient information for the estimation of object motion. Motion can be estimated continuously from each sensor through the analysis of the instantaneous state of an object. We have introduced a method based on Moore-Penrose pseudo-inverse theory to estimate the instantaneous state of an object. A linear feedback estimation algorithm is discussed to estimate the object 3D motion. Then, the motion estimated from each sensor is fused to provide more accurate and reliable information about the motion of an unknown object. The techniques of multisensor data fusion can be categorized into three methods: averaging, decision, and guiding. We present a fusion algorithm which combines averaging and decision.

• 제어로봇시스템학회:학술대회논문집
• /
• 1995.10a
• /
• pp.174-177
• /
• 1995

Detecting a small threat object either fast moving or floating on shallow water presents a formidable challenge to shipboard sensor systems, which must determine whether or not to launch defensive weapons in a timely manner. An integrated multisensor concept is envisioned wherein the combined use of active and passive sensor is employed for the detection of short duration targets in dense ocean surface clutter to maximize detection range. The objective is to develop multisensor integration techniques that operate on detection data prior to track formation while simultaneously fusing contacts to tracks. In the system concept, detections from a low grazing angle search radar render designations to a sensor-search infrared sensor for target classification which in turn designates an active electro-optical sensor for sector search and target verification.