• 한국생산제조학회지
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• 제21권3호
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• pp.465-472
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• 2012

This paper describes a new robust control algorithm which can be used to enhance the positioning performance of an ultra-precision positioning system. The working table is supported by flexure hinges and moved by a piezoelectric actuator, whose position is measured by an ultra-precise linear encoder. The system dynamics is very complicated because the movement of the table is governed by both the mechanical characteristics and those of the PZT actuator. So that, the dynamics of the stage was modeled roughly in this paper, and the overall system was formularized to solve the small gain problem. A series of experiments was conducted in order to verify the usefulness of the proposed algorithm. From the experimental results, the positioning performance such as the accuracy, the rise time and the hysteresis nonlinearity were greatly improved.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2018년도 추계학술대회
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• pp.641-642
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• 2018

현재 실내 측위를 위한 기술로 전파 지문 기반의 측위 기술의 성능은 데이터 비교 알고리즘의 선택에 따라 큰 영향을 받는다. 이때 학습 구조에 필요한 데이터 확장 기법에 의해 실내 측위의 정확도가 크게 개선될 수 있다. 본 논문에서는 Wi-Fi 전파 지문을 기반으로 하는 학습 구조를 구성하기 위해 학습 데이터의 구분 및 확장 기술을 통해 실제 측위에 적용할 수 있는 학습 구조의 중요성을 논의한다.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2006년도 International Symposium on GPS/GNSS Vol.2
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• pp.247-250
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• 2006

Based on the possibility theory and the fuzzy set, the Maximum Possibility Estimation method and its applications in kinematic GPS positioning are presented in this paper. Firstly, the principle and the optimal criterion of the Maximum Possibility Estimation method are explained. Secondly, the kinematic GPS positioning model of single epoch single frequency with baseline length constraint is developed. Then, the authors introduce the artificial immune algorithm and use this algorithm to search the global optimum of the Maximum Possibility Estimation model. The results of some examples show that the method is efficient for kinematic GPS positioning.

• International Journal of Advanced Culture Technology
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• 제9권4호
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• pp.302-306
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• 2021

Indoor localization is one of the basic elements of Location-Based Service, such as indoor navigation, location-based precision marketing, spatial recognition of robotics, augmented reality, and mixed reality. We propose a Voxel Labeling-based visual positioning system using object simultaneous localization and mapping (SLAM). Our method is a method of determining a location through single image 3D cuboid object detection and object SLAM for indoor navigation, then mapping to create an indoor map, addressing it with voxels, and matching with a defined space. First, high-quality cuboids are created from sampling 2D bounding boxes and vanishing points for single image object detection. And after jointly optimizing the poses of cameras, objects, and points, it is a Visual Positioning System (VPS) through matching with the pose information of the object in the voxel database. Our method provided the spatial information needed to the user with improved location accuracy and direction estimation.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 Proceedings of the Korea Automatic Control Conference, 11th (KACC); Pohang, Korea; 24-26 Oct. 1996
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• pp.129-132
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• 1996

There is a tendency nowadays to produce increasingly miniaturized electronic equipment which incorporate parts that have to be precisely positioned, like lenses, heads and CCD's in scanners, printers, copiers, VCR's, optical fiber modules, etc. In contrast to the production process of precision parts, which is currently being carried out automatically, the assemblage process is still being performed by specially skilled technicians. The assemblage process comprises normally the following steps: firstly, the parts are roughly positioned and partially fixed, secondly, the parts are manually nudged towards the target position and finally glued, screwed or welded. This paper presents a system that uses six piezo Impact Drive Mechanisms for accurate micro positioning within three degrees of freedom (lateral and longitudinal translation and rotation). The system is designed to positioning a printed circuit board with an accuracy better than 3 .mu.m (for translations), 5 mrad (for rotation).

• ETRI Journal
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• 제38권3호
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• pp.560-567
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• 2016

To solve the problem of parameter optimization in image sensor-based visible light positioning systems, theoretical limits for both the location and the azimuth angle of the image sensor receiver (ISR) are calculated. In the case of a typical indoor scenario, maximum likelihood estimations for both the location and the azimuth angle of the ISR are first deduced. The Cramer-Rao Lower Bound (CRLB) is then derived, under the condition that the observation values of the image points are affected by white Gaussian noise. For typical parameters of LEDs and image sensors, simulation results show that accurate estimates for both the location and azimuth angle can be achieved, with positioning errors usually on the order of centimeters and azimuth angle errors being less than $1^{\circ}$. The estimation accuracy depends on the focal length of the lens and on the pixel size and frame rate of the ISR, as well as on the number of transmitters used.

• 한국전자통신학회논문지
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• 제11권5호
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• pp.451-458
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• 2016

도시철도는 지상과 지하 구간으로 분류할 수 있으며, 특히 수도권 중심부는 대부분이 지하 역사 및 터널로 구축되어 있다. 지하 구간은 지상과 달리 GPS 수신이 불가능하여 위치 측정이 힘들기 때문에 실내 측위 기술 적용이 필요하다. 이를 위하여 본 연구에서는 Wi-Fi 기반과 이동통신 기지국 기반의 측위 기술에 대하여 분석하였다. 특히 도시철도 지하구간에는 핸드폰 통신을 위한 이동통신 기지국 정보를 활용한 기술을 연구하였으며, 핵심적인 기술로 터널 구간에서의 Hand-off 지점 간 변화되는 기지국 ID정보를 측위 기술에 적용하는 방안을 연구하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권4호
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• pp.1200-1215
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• 2023

With the development of IoT and artificial intelligence, location-based services are getting more and more attention. For solving the current problem that indoor positioning error is large and generalization is poor, this paper proposes a Model Stacking Algorithm for Indoor Positioning System using WiFi fingerprinting. Firstly, we adopt a model stacking method based on Bayesian optimization to predict the location of indoor targets to improve indoor localization accuracy and model generalization. Secondly, Taking the predicted position based on model stacking as the observation value of particle filter, collaborative particle filter localization based on model stacking algorithm is realized. The experimental results show that the algorithm can control the position error within 2m, which is superior to KNN, GBDT, Xgboost, LightGBM, RF. The location accuracy of the fusion particle filter algorithm is improved by 31%, and the predicted trajectory is close to the real trajectory. The algorithm can also adapt to the application scenarios with fewer wireless access points.

• 한국정밀공학회지
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• 제17권12호
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• pp.20-25
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• 2000

• Journal of Positioning, Navigation, and Timing
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• 제6권2호
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• pp.79-86
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• 2017

Precise Point Positioning-Real Time Kinematic (PPP-RTK) refers to a technology that combines PPP with network-RTK in which a user does not directly receive observed data from a reference station but receives State-Space Representation (SSR) messages corrected for error components from a central processing station through Networked Transport of RTCM via Internet Protocol (NTRIP) or Digital Multimedia Broadcasting (DMB) for purposes of positioning. SSR messages, which refer to corrections used in PPP-RTK, are generated by a central processing station using real-time observed data collected from reference stations and account for corrections needed due to the ionosphere, troposphere, satellite orbital errors, satellite time offsets, and satellite biases. This study used a type of SSR message provided in South Korea, known as Korea-SSR (K-SSR), to implement a PPP-RTK algorithm based on code-pseudorange measurements and validated its accuracy within the reference station network. In order to validate the accuracy of the implemented algorithm outside of the network, the K-SSR was extrapolated and applied to positioning in reference stations in Changchun, China (CHAN) and Japan (AIRA). This also entailed a quantitative evaluation that measured improvements in accuracy in comparison with point positioning. The results of the study showed that positioning applied with extrapolated K-SSR correction data was more accurate in both AIRA and CHAN than point positioning with improvements of approximately 20~50%.