• Journal of Institute of Control, Robotics and Systems
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• v.14 no.12
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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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• v.10 no.2
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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.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.1
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• pp.121-131
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• 2015

Passive emitter localization is preferred to use a small number of receivers as possible for the efficiency of strategic management in the field of modern electronic warfare support. Accurate emitter localization can be expected when utilizing continuous measurable parameters and a appropriate combination of theirs. For this reason, we compare CRLB (Cramer-Rao lower bound) of two moving platform with various measurable parameters to choose a appropriate combination of parameters for a better localization performance. And we propose the passive emitter localization method based on Levenberg-Marquardt algorithm with combined TDOA/FDOA and DOA to achieve better accuracy of emitter localization which is located on the ground and stationary. In addition, we present a method for determining the initial emitter position for LM algorithm's input to avoid the divergence of estimation and local minimum.

• International Journal of Control, Automation, and Systems
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• v.5 no.2
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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.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.2
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• pp.164-172
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• 2008

This paper presents the efficient localization of a mobile robot traveling on the floor with tags installed, using the spatial and temporal information acquired from passive RFID environment. Compared to previous research, the proposed localization method can reduce the position estimation error and also cut down the initial cost tag installation cost. Basically, it is assumed that a mobile robot is traveling over a series of straight line segments, each 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, the velocity and position estimation of a mobile robot starting from a known position, which is valid for all segments except the first one. Second, for the first segment in which the starting position is unknown, the velocity and position estimation is made possible by enforcing a mobile robot to traverse at least two tags at a constant velocity with the steering angle unchanged. Third, through experiments using our passive RFID localization system, the validity and performance of the mobile robot localization proposed in this paper is demonstrated.

• Journal of Ocean Engineering and Technology
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• v.34 no.3
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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.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.1
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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.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.9
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• pp.157-164
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• 2014

The accuracy of instantaneous passive emitter localization varies with sensing platforms and measurable parameters. Appropriate combination of instantaneous measurable parameters have more accurate localization performance than a single parameter based localization in general. Emitter localization is preferred to use a small number of receivers as possible for the efficiency of strategic management in the field of modern electronic warfare support. For this reason, we compare CRLB (Cramer-Rao lower bound) of two moving platform with various measurable parameters to search a appropriate choice of parameters for the better localization performance through the x-y axis CEP (circular error probable) derived form CLRB. In addition, we present the relation of the localization performance and accuracy of measurable parameters.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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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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• 2005.06a
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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.