• International Journal of Control, Automation, and Systems
• /
• v.3 no.1
• /
• pp.1-14
• /
• 2005

This paper presents an obstacle detection algorithm based on the consecutive and the cooperative range sensor scanning schemes. For a known environment, a mobile robot scans the surroundings using a range sensor that can rotate 3600°. The environment is rebuilt using nodes of two adjacent walls. The robot configuration is then estimated and an obstacle is detected by comparing characteristic points of the sensor readings. In order to extract edges from noisy and inaccurate sensor readings, a filtering algorithm is developed. For multiple robot localization, a cooperative scanning method with sensor range limit is developed. Both are verified with simulation and experiments.

• Proceedings of the IEEK Conference
• /
• 2002.07c
• /
• pp.1650-1653
• /
• 2002

In this paper, we propose the initial optimized structure of the Radial Basis function Networks that is simple in the part of the structure and fast converges more than neural networks with the analysis method using Time- Frequency Localization. We construct the hidden node with the Radial Basis functions their localization are similar with approximation target function in the plane of the Time and Frequency. We finally make a good decision of the initial structure for function approximation using genetic algorithm

• Journal of the Korea Institute of Military Science and Technology
• /
• v.13 no.1
• /
• pp.36-40
• /
• 2010

In this paper, the well-known LOB-based emitter localization using linear LSE algorithm is numerically implemented and the heuristic guidelines for the parameter values to achieve 1% RMS error are presented. In the simulation, we changed the total observation durations for LOB measurements, time interval between successive LOB measurements and sensor trajectories. The effects of the time interval of LOB measurements, the time duration of the LOB measurements and the angle of flight path arc on the performance are illustrated. The dependence of the performance on the various parameters is investigated and rule-of-thumbs for the parameter values corresponding to 1% RMS error are presented for each simulation condition.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2011.10a
• /
• pp.121-123
• /
• 2011

The accuracy of GPS varies depending on the number of GPS satellites and is declined in GPS interfering spot such as around forest or buildings. This paper proposes a localization improvement algorithm in GPS interfering spot by integrating information of multiple sensors in smartphone. The proposed algorithm is implemented in smartphone and the performance is evaluated in campus area.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2010.11a
• /
• pp.837-840
• /
• 2010

Wireless Sensor Networks have been proposed for several location-dependent applications. For such systems, the cost and limitations of the hardware on sensing nodes prevent the use of range-based localization schemes that depend on absolute point to point distance estimates. Because coarse accuracy is sufficient for most sensor network applications, solutions in range-free localization are being pursued as a cost-effective alternative to more expensive range-based approaches. In this paper, we proposed a Coefficient Allocated DV-Hop (CA DV-Hop) algorithm which reduces node's location error by awarding a credit value with respect to number of hops of each anchor to an unknown node. Simulation results have verified the high estimation accuracy with our approach which outperforms the classical DV-Hop.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.11
• /
• pp.3145-3162
• /
• 2023

As an important technology of the internetwork, wireless sensor network technique plays an important role in indoor localization. Non-line-of-sight (NLOS) problem has a large effect on indoor location accuracy. A location algorithm based on improved particle filter and directional probabilistic data association (IPF-DPDA) for WSN is proposed to solve NLOS issue in this paper. Firstly, the improved particle filter is proposed to reduce error of measuring distance. Then the hypothesis test is used to detect whether measurements are in LOS situations or NLOS situations for N different groups. When there are measurements in the validation gate, the corresponding association probabilities are applied to weight retained position estimate to gain final location estimation. We have improved the traditional data association and added directional information on the original basis. If the validation gate has no measured value, we make use of the Kalman prediction value to renew. Finally, simulation and experimental results show that compared with existing methods, the IPF-DPDA performance better.

• 제어로봇시스템학회:학술대회논문집
• /
• 2002.10a
• /
• pp.84.3-84
• /
• 2002

$\textbullet$ iconic localization method $\textbullet$ initial robot pose estimation $\textbullet$ genetic algorithms $\textbullet$ map matching

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.33 no.9B
• /
• pp.832-844
• /
• 2008

Distributed localization algorithms are necessary for large-scale wireless sensor network applications. In this paper, we introduce an efficient node distribution based localization algorithm that emphasizes simple refinement and low system load for low-cost and low-rate wireless sensors. Each node adaptively chooses neighbor nodes for sensors, update its position estimate by minimizing a local cost function and then passes this update to the neighbor nodes. The update process considers a distribution of nodes for large-scale networks which have same density in a unit area for optimizing the system performance. Neighbor nodes are selected within a range which provides the smallest received signal strength error based on the real experiments. MATLAB simulation showed that the proposed algorithm is more accurate than trilateration and les complex than multidimensional scaling. The implementation on MicaZ using TinyOS-2.x confirmed the practicality of the proposed algorithm.

• Journal of Institute of Control, Robotics and Systems
• /
• v.20 no.1
• /
• pp.70-77
• /
• 2014

This paper proposes a novel localization algorithm based on ego-motion which used Lucas-Kanade Optical Flow and warping image obtained through fish-eye lenses mounted on the robots. The omnidirectional image sensor is a desirable sensor for real-time view-based recognition of a robot because the all information around the robot can be obtained simultaneously. The preprocessing (distortion correction, image merge, etc.) of the omnidirectional image which obtained by camera using reflect in mirror or by connection of multiple camera images is essential because it is difficult to obtain information from the original image. The core of the proposed algorithm may be summarized as follows: First, we capture instantaneous $360^{\circ}$ panoramic images around a robot through fish-eye lenses which are mounted in the bottom direction. Second, we extract motion vectors using Lucas-Kanade Optical Flow in preprocessed image. Third, we estimate the robot position and angle using ego-motion method which used direction of vector and vanishing point obtained by RANSAC. We confirmed the reliability of localization algorithm using ego-motion based on fisheye warping image through comparison between results (position and angle) of the experiment obtained using the proposed algorithm and results of the experiment measured from Global Vision Localization System.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.45 no.4
• /
• pp.84-91
• /
• 2008

This paper suggests the robust localization system in the application of ambient display with multiple ultrasonic range sensors. The ambient display provides the interactive image and video to improve the quality of life, especially for low mobility elders. Due to the limitation of indoor localization, this paper employs linear prediction algorithm to recover the missing information based on AR(Autoregressive) model by using Yule-Walker method. Numerous speculations from prediction error and computation load are considered to decide the optimal length of referred data and order. The results of these analyses demonstrate that the linear prediction algorithm with the 16th order and 50 reference data can improve reliability of the system in average 74.39% up to 97.97% to meet the performance of interactive system.