• 제어로봇시스템학회논문지
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• 제14권12호
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• pp.1294-1301
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• 2008

Under passive RFID environment, this paper presents a new localization of a mobile robot traversing over the floor covered with tags, which is superior to existing methods in terms of estimation performance and cost effectiveness. Basically, it is assumed that a mobile robot is traveling along a series of straight line segments, each segment at a certain constant velocity, and that the number of tags sensed by a mobile robot at each sampling instant is at most one. First, for a given line segment with known starting point, the velocity and position of a mobile robot is estimated using the spatial and temporal information acquired from the traversed tag. Some discussions are made on the validity of the basic assumptions and the localization for the initial segment with unknown starting point. Second, for a given tag distribution density, the optimal tag arrangement is considered to reduce the position estimation error as well as to make easy the tag attachment on the floor. After reviewing typical tag arrangements, the pseudorandom tag arrangement is devised inspired from the Sudoku puzzle, a number placement puzzle. Third, through experiments using our passive RFID localization system, the validity and performance of the mobile robot localization proposed in this paper is demonstrated.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권2호
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• pp.703-722
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• 2016

Passive wireless devices have increasing civilian and military applications, especially in the scenario with wearable devices and Internet of Things. In this paper, we study indoor localization of a target equipped with radio-frequency identification (RFID) device in ultra-wideband (UWB) wireless networks. With known room layout, deterministic multipath components, including the line-of-sight (LOS) signal and the reflected signals via multipath propagation, are employed to locate the target with one transmitter and a single inaccurate receiver. A factor graph corresponding to the joint posterior position distribution of target and receiver is constructed. However, due to the mixed distribution in the factor node of likelihood function, the expressions of messages are intractable by directly applying belief propagation on factor graph. To this end, we approximate the messages by Gaussian distribution via minimizing the Kullback-Leibler divergence (KLD) between them. Accordingly, a parametric message passing algorithm for indoor passive localization is derived, in which only the means and variances of Gaussian distributions have to be updated. Performance of the proposed algorithm and the impact of critical parameters are evaluated by Monte Carlo simulations, which demonstrate the superior performance in localization accuracy and the robustness to the statistics of multipath channels.

• 전자공학회논문지
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• 제52권1호
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• pp.121-131
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• 2015

현대의 수동형 위치 추정(passive emitter localization) 분야에서는 수신기 운용의 효율성을 위해 적은 수의 수신기를 이용한 원거리 신호원의 정밀한 위치추정을 지향한다. 적은 수의 수신기로 정밀한 위치추정을 위해서 연속적이고, 측정 정보들의 복합적 활용을 통해 위치 추정 성능 향상을 기대할 수 있다. 본 논문에서는 제한된 환경의 2개의 이동하는 수신기 환경에서 측정 정보별 이론적 위치추정 성능을 분석하고, 위치추정 성능이 우수할 것으로 예상되는 TDOA (time difference of arrival)와 FDOA (frequency difference of arrival), 그리고 DOA (direction of arrival) 복합 측정 정보기반으로 비선형 최소자승법 중 하나인 LM (Levenberg-Marquardt) 방법을 이용하여 지상 고정 신호원의 위치를 추정하는 방법을 제안한다. 구체적으로 TDOA를 이용하여 산출한 방위 모호성이 포함된 DOA들을 이용하여 신호원의 초기 위치를 산출하고, 해당 신호원의 초기 위치와 T/F/DOA를 이용하여 위치추정 성능을 향상 시킨다.

• International Journal of Control, Automation, and Systems
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• 제5권2호
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• pp.200-207
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• 2007

The passive direction finding cross localization method is widely adopted in passive tracking, therefore there will exist masses of false intersection points. Eliminating these false intersection points correctly and quickly is a key technique in passive localization. A new method is proposed for passive locating and tracking multi-jammer target in this paper. It not only solves the difficulty of determining the number of targets when masses of false intersection points existing, but also solves the initialization problem of elastic network. Thus this method solves the problem of multi-jammer target correlation and the elimination of static false intersection points. The method which dynamically establishes multiple hypothesis trajectory trees solves the problem of eliminating the remaining false intersection points. Simulation results show that computational burden of the method is lower, the elastic network can more quickly find all or most of the targets and have a more probability of locking the real targets. This method can eliminate more false intersection points.

• 융합신호처리학회논문지
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• 제9권2호
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• pp.164-172
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• 2008

본 논문에서는 수동 RFID 환경에서 획득되는 위치 및 시간 정보를 이용하여, 태그가 배치된 노면을 주행하는 이동로봇의 위치를 효과적으로 추정하는 기법을 제안한다. 본 논문에서 제안된 위치 추정 기법은 기존 연구에 비해 위치 추정 오차를 감소시키고 또한 초기 태그 배치 비용을 감소시키는 효과가 있다. 기본적으로 이동로봇이 일련의 직선 구간을 구간별로 일정한 속도로 주행하며, 또한 매 순간 이동로봇에 의해 감지되는 태그의 수는 한 개 이하라고 가정한다. 첫째, 이미 알려진 지점을 출발하여 정속 직선 주행하는 이동로봇의 속도 및 위치를 추정하는 알고리즘을 개발하는데, 이는 최초를 제외한 여타 구간의 경우에 유효하다. 다음, 최초 구간의 경우 출발 위치를 모르는 상태이므로, 이동로봇이 두 개 이상의 태그를 정속 직선 주행하도록 하여 이동로봇의 속도 및 위치를 추정한다. 마지막으로, 제작된 수동 RFID 위치 추정 시스템을 이용한 실험을 통해 본 논문에서 제안된 이동로봇 위치 추정 기법의 유효성 및 성능을 입증한다.

• 한국해양공학회지
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• 제34권3호
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• pp.227-236
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• 2020

Underwater acoustics, which is the domain that addresses phenomena related to the generation, propagation, and reception of sound waves in water, has been applied mainly in the research on the use of sound navigation and ranging (SONAR) systems for underwater communication, target detection, investigation of marine resources and environment mapping, and measurement and analysis of sound sources in water. The main objective of remote sensing based on underwater acoustics is to indirectly acquire information on underwater targets of interest using acoustic data. Meanwhile, highly advanced data-driven machine-learning techniques are being used in various ways in the processes of acquiring information from acoustic data. The related theoretical background is introduced in the first part of this paper (Yang et al., 2020). This paper reviews machine-learning applications in passive SONAR signal-processing tasks including target detection/identification and localization.

• 제어로봇시스템학회논문지
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• 제12궈1호
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• pp.47-53
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• 2006

Localization is a essential technology for mobile robot to work well. Until now expensive sensors such as laser sensors have been used for mobile robot localization. We suggest RFID tag based localization system. RFID tag devices, antennas and tags are cheap and will be cheaper in the future. The RFID tag system is one of the most important elements in the ubiquitous system and RFID tag will be attached to all sorts of goods. Then, we can use this tags for mobile robot localization without additional costs. So, in this paper, the smart floor using passive RFID tags is proposed and, passive RFID tags are mainly used for identifying mobile robot's location and pose in the smart floor. We discuss a number of challenges related to this approach, such as tag distribution (density and structure), typing and clustering. When a mobile robot localizes in this smart floor, the localization error mainly results from the sensing range of the RFID reader, because the reader just ran know whether a tag is in the sensing range of the sensor. So, in this paper, we suggest two algorithms to reduce this error. We apply the particle filter based Monte Carlo localization algorithm to reduce the localization error. And with simulations and experiments, we show the possibility of our particle filter based Monte Carlo localization in the RFID tag based localization system.

• 전자공학회논문지
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• 제51권9호
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• pp.157-164
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• 2014

전자전 환경에서 위협 신호원의 순시 위치추정 성능은 위협과 다수의 수신기들의 기하학적 배치 및 측정 가능한 정보에 따라 가변되며, 상황에 맞는 복합적 정보의 선택 및 활용은 단일 제원을 이용한 경우보다 높은 위치추정 정확도를 가지는 것으로 알려져 있다. 현대의 전자전 지원(electronic warfare support) 분야에서는 전략적 운용의 효율성을 위해 적은 수의 수신기를 이용하여 원거리 위협의 정밀한 위치추정을 지향한다. 본 논문에서는 제한된 환경의 2개의 이동하는 수신기를 이용하여 고정 위협의 위치추정을 위해 측정 가능한 정보별 이론적 위치추정 성능을 도출 및 분석하고, 이를 기반으로 위치추정에 적합한 정보조합을 제시한다. 또한, 정보들의 변화에 따른 위치추정 성능을 분석 및 제시한다.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2005년도 춘계학술대회 논문집
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• pp.1034-1039
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• 2005

SALM(Simultaneous localization and mapping) and AI(Artificial intelligence) have been active research areas in robotics for two decades. In particular, localization is one of the most important tasks in mobile robot research. Until now expensive sensors such as a laser sensor have been used for mobile robot localization. Currently, the proliferation of RFID technology is advancing rapidly, while RFID reader devices, antennas and tags are becoming increasingly smaller and cheaper. So, in this paper, the smart floor using passive RFID tags is proposed and, passive RFID tags are mainly used for identifying location of the mobile robot in the smart floor. We discuss a number of challenges related to this approach, such as tag distribution (density and structure), typing and clustering. In the smart floor using RFID tags, the localization error results from the sensing area of the RFID reader, because the reader just knows whether the tag is in the sensing range of the sensor and, until now, there is no study to estimate the heading of mobile robot using RFID tags. So, in this paper, two algorithms are suggested to. The Markov localization method is used to reduce the location(X,Y) error and the Kalman Filter method is used to estimate the heading($\theta$) of mobile robot. The algorithms which are based on Markov localization require high computing power, so we suggest fast Markov localization algorithm. Finally we applied these algorithms our personal robot CMR-P3. And we show the possibility of our probability approach using the cheap sensors such as odometers and RFID tags for mobile robot localization in the smart floor

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1797-1801
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• 2005

SLAM(Simultaneous localization and mapping) and AI(Artificial intelligence) have been active research areas in robotics for two decades. In particular, localization is one of the most important issues in mobile robot research. Until now expensive sensors like a laser sensor have been used for the mobile robot's localization. Currently, as the RFID reader devices like antennas and RFID tags become increasingly smaller and cheaper, the proliferation of RFID technology is advancing rapidly. So, in this paper, the smart floor using passive RFID tags is proposed and, passive RFID tags are mainly used to identify the mobile robot's location on the smart floor. We discuss a number of challenges related to this approach, such as RFID tag distribution (density and structure), typing and clustering. In the smart floor using RFID tags, because the reader just can senses whether a RFID tag is in its sensing area, the localization error occurs as much as the sensing area of the RFID reader. And, until now, there is no study to estimate the pose of mobile robot using RFID tags. So, in this paper, two algorithms are suggested to. We use the Markov localization algorithm to reduce the location(X,Y) error and the Kalman Filter algorithm to estimate the pose(q) of a mobile robot. We applied these algorithms in our experiment with our personal robot CMR-P3. And we show the possibility of our probability approach using the cheap sensors like odometers and RFID tags for the mobile robot's localization on the smart floor.