• Journal of Communications and Networks
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• v.13 no.1
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• pp.6-11
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• 2011

In this paper, an enhanced localization algorithm based on double thresholds (LAD) is proposed for localizing narrowband signals in the frequency domain. A simplified LAD method is first studied to reduce the computational complexity of the original LAD method without performance loss. The upper and lower thresholds of the simplified LAD method are directly calculated by running the forward consecutive mean excision algorithm only once. By combining the simplified LAD method and binary morphological operators, the enhanced LAD method is then proposed and its performance is simply discussed. The simulation results verify the correctness of discussion and show that the enhanced LAD method is superior to the LAD with adjacent cluster combining method, especially at low signal-to-noise ratio.

• Journal of Ocean Engineering and Technology
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• v.38 no.3
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• pp.115-123
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• 2024

USBL systems are essential for providing accurate positions of autonomous underwater vehicles (AUVs). On the other hand, the accuracy can be degraded by outliers because of the environmental conditions. A failure to address these outliers can significantly impact the reliability of underwater localization and navigation systems. This paper proposes a novel outlier rejection algorithm for AUV localization using Voronoi diagrams and query point calculation. The Voronoi diagram divides data space into Voronoi cells that center on ultra-short baseline (USBL) data, and the calculated query point determines if the corresponding USBL data is an inlier. This study conducted experiments acquiring GPS and USBL data simultaneously and optimized the algorithm empirically based on the acquired data. In addition, the proposed method was applied to a sensor fusion algorithm to verify its effectiveness, resulting in improved pose estimations. The proposed method can be applied to various sensor fusion algorithms as a preprocess and could be used for outlier rejection for other 2D-based location sensors.

• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.9C
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• pp.834-839
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• 2007

The superimposed text in video brings important semantic clues into content analysis. In this paper, we present the new and fast superimposed text localization method in video segments. We detect the superimposed text by using temporal information contained in the video. To detect the superimposed text fast, we have minimized the candidate region of localizing superimposed texts by using the difference between consecutive frames. Experimental results are presented to demonstrate the good performance of the new superimposed text localization algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.80.2-80
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• 2002

The problem of sound source localization is to determine the position of sound sources using the measurement of the acoustic signals received by microphones. To develop a good sound source localization system which is applicable to a mobile platform such as robots, a time delay estimator with low computational complexity and robustness to background noise or reverberations is necessary. In this paper, an explicit adaptive time delay estimation method for a sound source localization system is proposed. Proposed explicit adaptive time estimation algorithm employs direct adaptation of the delay parameter using a transform-based optimization technique, rather than...

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2007.06a
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• pp.111-112
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• 2007

• The Journal of Korea Robotics Society
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• v.16 no.4
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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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.5
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• pp.407-412
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• 2014

Concerns and demands for the Location Based Services (LBS) using Global Positioning System (GPS) and Wi-Fi are largely increased in the world in the present. In some experimental results, it was noted that many errors are frequently occurred when the distances between an anchor node and a mobile node acre measured in indoor localization environment of Wireless Personal Area Network (WPAN). In this paper, localization compensation algorithm based on maximum measurement value ($LCA_{MMV}$) for moving objects in WPAN is proposed, and the performance of the algorithm is analyzed by experiments on three scenarios for movement of mobile nodes. From the experiments, it was confirmed that the average localization accuracy of suggested algorithm was more increased than Symmetric Double-Sided Two-Way Ranging (SDS-TWR) and triangulation as average 40.9cm, 77.6cm and 6.3cm, respectively on scenario 1-3.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.4B
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• pp.309-316
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• 2012

The compensation algorithm for localization using the least-squires method in NLOS(Non Line of Sight) environment is suggested and the performance of the algorithm is analyzed in this paper. In order to improve the localization correction rate of the moving node, 1) the distance value of the moving node that is moving as an constant speed is measured by SDS-TWR(Symmetric Double-Sided Two-Way Ranging); 2) the location of the moving node is measured using the triangulation scheme; 3) the location of the moving node measured in 2) is compensated using the least-squares method. By the experiments in NLOS environment, it is confirmed that the average localization error rates are measured to ${\pm}1m$, ${\pm}0.2m$ and ${\pm}0.1m$ by the triangulation scheme, the Kalman filter and the least-squires method respectively. As a result, we can see that the localization error rate of the suggested algorithm is higher than that of the triangulation as average 86.0% and the Kalman filter as average 16.0% respectively.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.857-860
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• 2008

In this paper, we proposed cooperative based localization algorithm for wireless sensor networks, which can estimate to unknown node position using received signal strength table set. The unknown nodes are monitor to RSS from neighbor nodes and exclude existence possibility area of sensor node in sensor field. Finally, we can calculate the centroid position for each unknown node with cooperative localization algorithm. Furthermore, these processes are applied iteratively about all nodes which is recorded to RSS table and can estimate for unknown nodes.