• Ocean and Polar Research
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• v.30 no.2
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• pp.129-140
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• 2008

As a preliminary effort to establish a data assimilative ocean forecasting system, we reviewed the theory of the Ensemble Kamlan Filter (EnKF) and developed practical techniques to apply the EnKF algorithm in a real ocean circulation modeling system. To verify the performance of the developed EnKF algorithm, a wind-driven double gyre was established in a rectangular ocean using the Regional Ocean Modeling System (ROMS) and the EnKF algorithm was implemented. In the ideal ocean, sea surface temperature and sea surface height were assimilated. The results showed that the multivariate background error covariance is useful in the EnKF system. We also tested the sensitivity of the EnKF algorithm to the localization and inflation of the background error covariance and the number of ensemble members. In the sensitivity tests, the ensemble spread as well as the root-mean square (RMS) error of the ensemble mean was assessed. The EnKF produces the optimal solution as the ensemble spread approaches the RMS error of the ensemble mean because the ensembles are well distributed so that they may include the true state. The localization and inflation of the background error covariance increased the ensemble spread while building up well-distributed ensembles. Without the localization of the background error covariance, the ensemble spread tended to decrease continuously over time. In addition, the ensemble spread is proportional to the number of ensemble members. However, it is difficult to increase the ensemble members because of the computational cost.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.9
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• pp.362-369
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• 2012

Recently, a target detection based on a distributed sensor network has been much studied in active sonar. Zhou et al. proposed a target localization method using line fitting based on a distributed sensor network which consists of low complexity sensors that only report binary detection results. This method has three advantages relative to ML estimator. First, there is no need to estimate propagation model parameters. Second, the computation is simple. Third, it only use sensors with "detection", which implies less data to be collected by data processing center. However, this method has larger target localization error than the ML estimator. In this paper, a target localization method which modifies Zhou's method is proposed for reducing the localization error. The modified method shows the performance improvement that the target localization error is reduced by 40.7% to Zhou's method in the point of RMSE.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.7
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• pp.734-741
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• 2009

A new localization algorithm is proposed for a fast moving mobile robot, which utilizes only one beacon and the global features of the differential-driving mobile robot. It takes a relatively long time to localize a mobile robot with active beacon sensors since the distance to the beacon is measured by the traveling time of the ultrasonic signal. When the mobile robot is moving slowly the measurement time does not yield a high error. At a higher mobile robot speed, however, the localization error becomes too large to locate the mobile robot. Therefore, in high-speed mobile robot operations, instead of using two or more active beacons for localization, only one active beacon and the global features of the mobile robot are used to localize the mobile robot in this research. The two global features are the radius and center of the rotational motion for the differential-driving mobile robot which generally describe motion of the mobile robot and are used for the trace prediction of the mobile robot. In high speed operations the localizer finds an intersection point of this predicted trace and a circle which is centered at the beacon and has the radius of the distance between the mobile robot and the beacon. This new approach resolves the large localization error caused by the high speed of the mobile robot. The performance of the new localization algorithm has been verified through the experiments with a high-speed mobile robot.

• 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

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

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.8
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• pp.678-687
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• 2005

This paper describes a relative localization algorithm using odometry data and consecutive local maps. The purpose of this paper is the odometry error correction using the area matching of two consecutive local maps. The local map is built up using a sensor module with dual laser beams and USB camera. The range data form the sensor module is measured using the structured lighting technique (active stereo method). The advantage in using the sensor module is to be able to get a local map at once within the camera view angle. With this advantage, we propose the AVS (Aligned View Sector) matching algorithm for. correction of the pose error (translational and rotational error). In order to evaluate the proposed algorithm, experiments are performed in real environment.

• Journal of IKEEE
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• v.20 no.1
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• pp.1-8
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• 2016

In this paper, we propose an effective time-of-arrival (TOA)-based localization method with adaptive bias computation in indoor environments. The goal of the localization is to estimate an accurate target's location in wireless localization system. However, in indoor environments, non-line-of-sight (NLOS) errors block the signal propagation between target device and base station. The NLOS errors have significant effects on ranging between two devices for wireless localization. In TOA-based localization, finding the target's location inside the overlapped area in the TOA-circles is difficult. We present an effective localization method using compensated distance with adaptive bias computation. The proposed method is possible for the target's location to estimate an accurate location in the overlapped area using the measured distances with subtracted adaptive bias. Through localization experiments in indoor environments, estimation error is reduced comparing to the conventional localization methods.

• The Journal of the Acoustical Society of Korea
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• v.36 no.3
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• pp.218-227
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• 2017

In this paper, an algorithm to calculate both bearing and distance error for target detection and localization is proposed using the Cramer Rao lower bound to estimate the minium variance of their error in DOA (Direction Of Arrival) estimation. The performance of arrays in detection and localization depends on the accuracy of DOA, which is affected by a variation of SNR (Signal to Noise Ratio). The SNR is determined by sonar parameters such as a SL (Source Level), TL (Transmission Loss), NL (Noise Level), array shape and beam steering angle. For verification of the suggested method, a Monte Carlo simulation was performed to probabilistically calculate the bearing and distance error according to the SNR which varies with the relative position of the target in space and noise level.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.12
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• pp.1046-1052
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• 2016

For the safe driving of autonomous vehicles, accurate position estimation is required. Generally, position error must be less than 1m because of lane keeping. However, GPS positioning error is more than 1m. Therefore, we must correct this error and a map matching algorithm is generally used. Especially, road marking intensity map have been used in many studies. In previous work, 3D LIDAR with many vertical layers was used to generate a local intensity map. Because it can be obtained sufficient longitudinal information for map matching. However, it is expensive and sufficient road marking information cannot be obtained in rush hour situations. In this paper, we propose a localization algorithm using an accumulated intensity local map. An accumulated intensity local map can be generated with sufficient longitudinal information using 3D LIDAR with a few vertical layers. Using this algorithm, we can also obtain sufficient intensity information in rush hour situations. Thus, it is possible to increase the reliability of the map matching and get accurate position estimation result. In the experimental result, the lateral RMS position error is about 0.12m and the longitudinal RMS error is about 0.19m.

• 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.