• Journal of the Korea Institute of Military Science and Technology
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• v.19 no.1
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• pp.9-15
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• 2016

Source localization has developed in various fields of signal processing including radar, sonar, and wireless communication, etc. Source localization can be found by estimating the time difference of arrival between the each of sensors. Several methods like the NLS(Nonlinear Least Square) cost function have been proposed in order to improve the performance of time delay estimation. In this paper, we propose a clustering method using the four sensors with the same aperture as previous methods of using the three sensors. Clustering method can be improved the source localization performance by grouping similar estimated values. The performance of source localization using clustering method is evaluated by Monte Carlo simulation.

• The Journal of Korea Robotics Society
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• v.9 no.3
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• pp.154-159
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• 2014

The Global Positioning System (GPS) is widely used to aid the navigation of aerial vehicles. However, the GPS cannot be used indoors, so alternative navigation methods are needed to be developed for micro aerial vehicles (MAVs) flying in GPS-denied environments. In this paper, a real-time three-dimensional (3-D) indoor navigation system and closed-loop control of a quad-rotor aerial vehicle equipped with an inertial measurement unit (IMU) and a low-cost light detection and ranging (LIDAR) is presented. In order to estimate the pose of the vehicle equipped with the two-dimensional LIDAR, an octree-based grid map and Monte-Carlo Localization (MCL) are adopted. The navigation results using the MCL are then evaluated by making a comparison with a motion capture system. Finally, the results are used for closed-loop control in order to validate its positioning accuracy during procedures for stable hovering and waypoint-following.

• Tunnel and Underground Space
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• v.32 no.3
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• pp.231-242
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• 2022

In this study, we developed a robot operating system (ROS)-based autonomous driving robot that estimates the robot's position in underground mines and drives and returns through multiple waypoints. Autonomous driving robots utilize SLAM (Simultaneous Localization And Mapping) technology to generate global maps of driving routes in advance. Thereafter, the shape of the wall measured through the LiDAR sensor and the global map are matched, and the data are fused through the AMCL (Adaptive Monte Carlo Localization) technique to correct the robot's position. In addition, it recognizes and avoids obstacles ahead through the LiDAR sensor. Using the developed autonomous driving robot, experiments were conducted on indoor experimental sites that simulated the underground mine site. As a result, it was confirmed that the autonomous driving robot sequentially drives through the multiple waypoints, avoids obstacles, and returns stably.

• 한국전기화학회:학술대회논문집
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• 2004.06a
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• pp.287-292
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• 2004

In order to understand the relationship between molecular structure of Metal-Organic Framework(MOF) and capacity of hydrogen absorption, quantum mechanical calculations and grand canonical Monte Carlo simulations have been carried out on a series of MOF-5 having various organic linkers. The calculation results about specific surface area and electron density for various frameworks indicated that the capacity of the hydrogen storage is largely dependent on effective surface area rather than the free volume. Based on the iso-electrostatic potential surface from density functional calculation and the amount of adsorbed hydrogens from grand canonical Monte Carlo calculation, it was also found that the electron localization ground organic linker plays an important role in hydrogen capacity of MOFs.

• The Journal of Korea Robotics Society
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• v.9 no.1
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• pp.28-38
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• 2014

This paper verifies the performance of Extended Kalman Filter(EKF) and MCL(Monte Carlo Localization) approach to localization of an underwater vehicle through experiments. Especially, the experiments use acoustic range sensor whose measurement accuracy and uncertainty is not yet proved. Along with localization, the experiment also discloses the uncertainty features of the range measurement such as bias and variance. The proposed localization method rejects outlier range data and the experiment shows that outlier rejection improves localization performance. It is as expected that the proposed method doesn't yield as precise location as those methods which use high priced DVL(Doppler Velocity Log), IMU(Inertial Measurement Unit), and high accuracy range sensors. However, it is noticeable that the proposed method can achieve the accuracy which is affordable for correction of accumulated dead reckoning error, even though it uses only range data of low reliability and accuracy.

• ETRI Journal
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• v.37 no.3
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• pp.533-540
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• 2015

In wireless localization, several linear closed-form solution (LCS) methods have been investigated as a direct result of the drawbacks that plague the existing iterative methods, such as the local minimum problem and heavy computational burden. Among the known LCS methods, both the direct solution method and the difference of squared range measurements method are considered in this paper. These LCS methods do not have any of the aforementioned problems that occur in the existing iterative methods. However, each LCS method does have its own individual error property. In this paper, a hybrid LCS method is presented to reduce these errors. The hybrid LCS method integrates the two aforementioned LCS methods by using two check points that give important information on the probability of occurrence of each LCS's individual error. The results of several Monte Carlo simulations show that the proposed method has a good performance. The solutions provided by the proposed method are accurate and reliable. The solutions do not have serious errors such as those that occur in the conventional standalone LCS and iterative methods.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.7
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• pp.633-639
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• 2013

Several estimation methods used in the range measurement based wireless localization area have individual problems. These problems may not occur according to certain application areas. However, these problems may give rise to serious problems in particular applications. In this paper, three methods, ILS (Iterative Least Squares), DS (Direct Solution), and DSRM (Difference of Squared Range Measurements) methods are considered. Problems that can occur in these methods are defined and a simple hybrid solution is presented to solve them. The ILS method is the most frequently used method in wireless localization and has local minimum problems and a large computational burden compared with closed-form solutions. The DS method requires less processing time than the ILS method. However, a solution for this method may include a complex number caused by the relations between the location of reference nodes and range measurement errors. In the near-field region of the complex solution, large estimation errors occur. In the DSRM method, large measurement errors occur when the mobile node is far from the reference nodes due to the combination of range measurement error and range data. This creates the problem of large localization errors. In this paper, these problems are defined and a hybrid localization method is presented to avoid them by integrating the DS and DSRM methods. The defined problems are confirmed and the performance of the presented method is verified by a Monte-Carlo simulation.

• The Journal of the Korea institute of electronic communication sciences
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• v.6 no.1
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• pp.148-156
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• 2011

This paper presents an implementation of autonomous navigation of a mobile robot indoors. It explains methods for map building, localization, obstacle avoidance and path planning. Geometric map is used for localization and path planning. The localization method calculates sensor data based on the map for comparison with the real sensor data. Monte Carlo Localization(MCL) method is adopted for estimation of the robot position. For obstacle avoidance, an artificial potential field generates repulsive and attractive force to the robot. Dijkstra algorithm plans the shortest distance path from a start position to a goal point. The methods integrate into autonomous navigation method and implemented for indoor navigation. The experiments show that the proposed method works well for safe autonomous navigation.

• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.9
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• pp.1060-1068
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• 2016

The range-free localization using connectivity information has problems of mobile tracking. This paper proposes two Bayesian filter-based mobile tracking algorithms considering a propagation scenario. Kalman and Markov Chain Monte Carlo (MCMC) particle filters are applied according to linearity of two measurement models. Measurement models of the Kalman and MCMC particle filter-based algorithms respectively are defined as connectivity between mobiles, information fusion of connectivity information and received signal strength (RSS) from neighbors within one-hop. To perform the accurate simulation, we consider a real indoor map of shopping mall and degree of radio irregularity (DOI) model. According to obstacles between mobiles, we assume two types of DOIs. We show the superiority of the proposed algorithm over existing range-free algorithms through MATLAB simulations.