• 한국군사과학기술학회지
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• 제19권1호
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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.

• Nuclear Engineering and Technology
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• 제56권4호
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• pp.1153-1164
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• 2024

In recent years, unmanned ground vehicles (UGVs) have been used to search for lost or stolen radioactive sources to avoid radiation exposure for operators. To achieve autonomous localization of radioactive sources, the UGVs must have the ability to automatically determine the next radiation measurement location instead of following a predefined path. Also, the radiation field of radioactive sources has to be reconstructed or inverted utilizing discrete measurements to obtain the radiation intensity distribution in the area of interest. In this study, we propose an effective source localization framework and method, in which UGVs are able to autonomously explore in the radiation area to determine the location of radioactive sources through an iterative process: path planning, radiation field reconstruction and estimation of source location. In the search process, the next radiation measurement point of the UGVs is fully predicted by the design path planning algorithm. After obtaining the measurement points and their radiation measurements, the radiation field of radioactive sources is reconstructed by the Gaussian process regression (GPR) model based on machine learning method. Based on the reconstructed radiation field, the locations of radioactive sources can be determined by the peak analysis method. The proposed method is verified through extensive simulation experiments, and the real source localization experiment on a Cs-137 point source shows that the proposed method can accurately locate the radioactive source with an error of approximately 0.30 m. The experimental results reveal the important practicality of our proposed method for source autonomous localization tasks.

• 제어로봇시스템학회논문지
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• 제22권2호
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• pp.104-109
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• 2016

In this paper, a localization method for multiple robots based on Bayesian inference is proposed when multiple robots adopting multi-RAT (Radio Access Technology) communications exist in cognitive radio networks. Multiple robots are separately defined by primary and secondary users as in conventional mobile communications system. In addition, the heterogeneous spectrum environment is considered in this paper. To improve the performance of localization for multiple robots, a realistic multiple primary user distribution is explained by using the probabilistic graphical model, and then we introduce the Gibbs sampler strategy based on Bayesian inference. In addition, the secondary user selection minimizing the value of GDOP (Geometric Dilution of Precision) is also proposed in order to overcome the limitations of localization accuracy with Gibbs sampling. Via the simulation results, we can show that the proposed localization method based on GDOP enhances the accuracy of localization for multiple robots. Furthermore, it can also be verified from the simulation results that localization performance is significantly improved with increasing number of observation samples when the GDOP is considered.

• Nuclear Engineering and Technology
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• 제38권7호
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• pp.619-638
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• 2006

Low temperature irradiation can significantly harden metallic materials and often lead to strain localization and ductility loss in deformation. This paper provides a review on the radiation effects on the deformation of metallic materials, focusing on microscopic and macroscopic strain localization phenomena. The types of microscopic strain localization often observed in irradiated materials are dislocation channeling and deformation twinning, in which dislocation glides are evenly distributed and well confined in the narrow bands, usually a fraction of a micron wide. Dislocation channeling is a common strain localization mechanism observed virtually in all irradiated metallic materials with ductility, while deformation twinning is an alternative localization mechanism occurring only in low stacking fault energy(SFE) materials. In some high stacking fault energy materials where cross slip is easy, curved and widening channels can be formed depending on dose and stress state. Irradiation also prompts macroscopic strain localization (or plastic instability). It is shown that the plastic instability stress and true fracture stress are nearly independent of irradiation dose if there is no radiation-induced phase change or embrittlement. A newly proposed plastic Instability criterion is that the metals after irradiation show necking at yield when the yield stress exceeds the dose-independent plastic instability stress. There is no evident relationship between the microscopic and macroscopic strain localizations; which is explained by the long-range back-stress hardening. It is proposed that the microscopic strain localization is a generalized phenomenon occurring at high stress.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권8호
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• pp.3889-3903
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• 2017

Fast and accurate localization of randomly deployed nodes is required by many applications in wireless sensor networks (WSNs). However, mobile nodes localization in WSNs is more difficult than static nodes localization since the nodes mobility brings more data. In this paper, we propose a Range-based Monte Carlo Box (RMCB) algorithm, which builds upon the Monte Carlo Localization Boxed (MCB) algorithm to improve the localization accuracy. This algorithm utilizes Received Signal Strength Indication (RSSI) ranging technique to build a sample box and adds a preset error coefficient in sampling and filtering phase to increase the success rate of sampling and accuracy of valid samples. Moreover, simplified Particle Swarm Optimization (sPSO) algorithm is introduced to generate new samples and avoid constantly repeated sampling and filtering process. Simulation results denote that our proposed RMCB algorithm can reduce the location error by 24%, 14% and 14% on average compared to MCB, Range-based Monte Carlo Localization (RMCL) and RSSI Motion Prediction MCB (RMMCB) algorithm respectively and are suitable for high precision required positioning scenes.

• 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.

• 로봇학회논문지
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• 제16권4호
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• pp.306-312
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• 2021

This paper describes localization of autonomous underwater vehicles(AUV), which can be used when some navigation sensor data are an outlier. In that situation, localization through existing navigation algorithms causes problems in long-range localization. Even if an outlier sensor data occurs once, problems of localization will continue. Also, if outlier sensor data is related to azimuth (direction of AUV), it causes bigger problems. Therefore, a parallel localization module, in which different algorithms are performed in a normal and abnormal situation should be designed. Before designing a parallel localization module, it is necessary to study an effective method in the abnormal situation. So, we propose a localization method through machine learning. For this method, a learning model consists of only Fully-Connected and trains through randomly contaminated real sea data. The ground truth of training is displacement between subsequent GPS data. As a result, average error in localization through the learning model is 0.4 times smaller than the average error in localization through the existing navigation algorithm. Through this result, we conclude that it is suitable for a component of the parallel localization module.

• ETRI Journal
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• 제37권2호
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• pp.262-272
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• 2015

In particular, for a practical mobile robot team to perform such a task as that of carrying out a search and rescue mission in a disaster area, the network connectivity and localization have to be guaranteed even in an environment where the network infrastructure is destroyed or a Global Positioning System is unavailable. This paper proposes the new collective intelligence network management architecture of multiple mobile robots supporting seamless network connectivity and cooperative localization. The proposed architecture includes a resource manager that makes the robots move around and not disconnect from the network link by considering the strength of the network signal and link quality. The location manager in the architecture supports localizing robots seamlessly by finding the relative locations of the robots as they move from a global outdoor environment to a local indoor position. The proposed schemes assuring network connectivity and localization were validated through numerical simulations and applied to a search and rescue robot team.

• 대한전자공학회논문지TC
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• 제48권2호
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• pp.72-78
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• 2011

물체의 위치정보는 많은 응용분야에 매우 유용한 요소를 제공한다. 현재 GPS가 이를 위하여 일반화되어 있으나, GPS의 실내측위, 비용 및 전력소모 등의 한계성을 극복하기 위해 최근 이동통신 망, 무선 센서 망 및 ad hoc 네트워크를 이용한 무선 측위 기술에 대한 연구가 활발한 진행되고 있다. 따라서 본고에서는 무선망에서 구현이 가능한 대표적인 무선 측위 기술들을 조사하고 이들의 측위 원리와 성능을 연구자료 들을 토대로 분석하였다. 결론적으로, 무선 측위 기술의 선택은 측위환경, 정확도, 소요시간, 계산 양, 구현의 용이성 등의 설계요소로부터 응용분야에 가장 적합한 요소들을 고려하여야 하겠다.

• 로봇학회논문지
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• 제11권4호
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• pp.205-216
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• 2016

The localization of the robot is one of the most important factors of navigating mobile robots. The use of featured information of landmarks is one approach to estimate the location of the robot. This approach can be classified into two categories: the natural-landmark-based and artificial-landmark-based approach. Natural landmarks are suitable for any environment, but they may not be sufficient for localization in the less featured or dynamic environment. On the other hand, artificial landmarks may generate shaded areas due to space constraints. In order to improve these disadvantages, this paper presents a novel development of the localization system by using artificial and natural-landmarks-based approach on a topological map. The proposed localization system can recognize far or near landmarks without any distortion by using landmark tracking system based on top-view image transform. The camera is rotated by distance of landmark. The experiment shows a result of performing position recognition without shading section by applying the proposed system with a small number of artificial landmarks in the mobile robot.