• 제목/요약/키워드: Multisensor

검색결과 60건 처리시간 0.028초

Segment-based Image Classification of Multisensor Images

  • Lee, Sang-Hoon
    • 대한원격탐사학회지
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    • 제28권6호
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    • pp.611-622
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    • 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.

Improvement of Control Performance by Data Fusion of Sensors

  • Na, Seung-You;Shin, Dae-Jung
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • 제4권1호
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    • pp.63-69
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    • 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.

지능 로봇 시스템을 위한 다중 센서 데이타 Fusion (Multisensor Data Fusion for Intelligent Robot Systems)

  • 김완주;고중협;정명진
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 1991년도 하계학술대회 논문집
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    • pp.787-794
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    • 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.

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A SHIPBOARD MULTISENSOR SOLUTION FOR THE DETECTON OF FAST MOVING SMALL SURFACE OBJECTS

  • Ko, Hanseok
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 1995년도 Proceedings of the Korea Automation Control Conference, 10th (KACC); Seoul, Korea; 23-25 Oct. 1995
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    • pp.174-177
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    • 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.

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Suboptimal Adaptive Filters for Stochastic Systems with Multisensor Environment

  • Shin, Vladimir;Ahn, Jun-Il
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 2004년도 ICCAS
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    • pp.2045-2050
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    • 2004
  • An optimal combination of arbitrary number correlated estimates is derived. In particular, for two estimates this combination represents the well-known Millman and Bar-Shalom-Campo formulae for uncorrelated and correlated estimation errors, respectively. This new result is applied to the various estimation problems as least-squares estimation, Kalman filtering, and adaptive filtering. The new approximate adaptive filter with a parallel structure is proposed. It is shown that this filter is very effective for multisensor systems containing different types of sensors. Examples demonstrating the accuracy of the proposed filter are given.

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멀티센서 데이터 융합에 의한 차륜형 이동체 위치추정시스템의 정도 개선에 관한 연구 (A Study on the Improvement of the Accuracy of a Wheeled Vehicle Positioning System by Multisensor Data Fusion)

  • 최진규;하윤수
    • Journal of Advanced Marine Engineering and Technology
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    • 제24권1호
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    • pp.119-126
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    • 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.

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Multisensor Image Fusion for Enhanced Coastal Wetland Mapping

  • Shanmugam, P.;Ahn, Yu-Hwan;Sanjeevi, S.;Yoo, Hong-Ryong
    • 대한원격탐사학회:학술대회논문집
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    • 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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    • pp.902-904
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    • 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.

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퍼지기법을 이용한 다중 센서 데이타 Fusion (Multisensor Data Fusion Using Fuzzy Techniques)

  • 김완주;고중협;정명진
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 1991년도 하계학술대회 논문집
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    • pp.781-786
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    • 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.

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동적환경에서 퍼지추론을 이용한 이동로봇의 다중센서기반의 자율주행 (Multisensor-Based Navigation of a Mobile Robot Using a Fuzzy Inference in Dynamic Environments)

  • 진태석;이장명
    • 한국정밀공학회지
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    • 제20권11호
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    • pp.79-90
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    • 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.