• Journal of information and communication convergence engineering
• /
• 제8권2호
• /
• pp.228-233
• /
• 2010

In this paper, the iteration localization algorithm that NLOS signal is iteratively removed to get the exact location in the wireless sensor network is proposed. To evaluate the performance of the proposed algorithm, TDOA location estimation method is used, and readers are located on every 150m intervals with rectangular shape in $300m{\times}300m$ searching field. In that searching field, the error distance is analyzed according to increasing the number of iteration, sub-blink and the estimated sensor node locations which are located in the iteration range. From simulation results, the error distance is diminished according to increasing the number of the sub-blink and iteration with the proposed location estimation algorithm in NLOS environment. Therefore, to get more accurate location information in wireless sensor network in NLOS environments, the proposed location estimation algorithm removing NLOS signal effects through iteration scheme is suitable.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2004년도 ICCAS
• /
• pp.202-205
• /
• 2004

This paper represents the role of pinna in localizing target direction. Specially, this paper described what is the role of right-side pinna versus left-side pinna. In this experiment, one side of the pinna function was distorted intentionally by inserting a short tube on the ear canal. The localization error caused by right and left side pinna distortion was investigated. Since a laser pointer showed much less error (0.5%) in localizing target position than FASTRAK (30%) that has been generally used, it was used for the pointing task. It was found that harmonic components were not essential for the auditory target localization, however, non-harmonic nearby frequency components were more important to localize target direction of sound. We have found that the right pinna is one of the most important functions in localizing target direction and pure tone with only one frequency component is confusing for localization.

• 대한기계학회논문집A
• /
• 제22권3호
• /
• pp.688-694
• /
• 1998

The ability to locate the defects in materials is one of the major attrations of the acoustic emission(AE) technique. The most conventional method for planar AE source localization is to place three or more AE sensors on the plate and to determine the source position by measuring the differences in the arrival times of the AE wave at the sensors, which is called as triangulation method. But this method can not be applied in the material of which elastic wave propagtion velocity is not known. In this paper, we propose two methods, vector method and error minimization method, for AE source location on the material with unknown AE wave velocity. In this method, it is not needed to know the propagation velocity previously, that is, we can apply this method to arbitrary material of which properties are not known exactly. Also, in this paper, the robustness to the error in the measurement of time differences are discussed for both methods. Finally, in order to evaluate the actual performances, experiments using a pencil lead break as the AE source were carried out on the aluminum plate.

• 제어로봇시스템학회논문지
• /
• 제14권8호
• /
• pp.752-759
• /
• 2008

In this paper, we propose a high accurate geo-location system based on a single base station, where its location is obtained by Time-of-Arrival(ToA) and Direction-of-Arrival(DoA) of the radio signal. For estimating accurate ToA and DoA information, a MUltiple SIgnal Classification(MUSIC) is adopted. However, the estimation of ToA and DoA using MUSIC algorithm is a time-consuming process. The position tracking bias is occurred by the time delay caused by the estimation process. In order to mitigate the bias error, we propose the estimation method of the position tracking bias and compensate the location error produced by the time delay using the position tracking bias mitigation. For accurate self-localization of mobile robot, the Unscented Kalman Filter(UKF) with position tracking bias is applied. The simulation results show the efficiency and accuracy of the proposed geo-location system and the enhanced performance when the Unscented Kalman Filter is adopted for mobile robot application.

• 동력기계공학회지
• /
• 제17권5호
• /
• pp.100-111
• /
• 2013

Indoor real-time positioning for multiple targets is required to realize human-robot symbiosis. This study firstly presents positioning accuracy on an autonomous mobile robot controlled by 3-D coordinates that is obtained by a real-time indoor positioning system with spread spectrum (SS) ultrasonic signals communicated by code-division multiple access. Although many positioning systems have been investigated, the positioning system with the SS ultrasonic signals can measure identified multiple 3-D positions in every 70 ms with noise tolerance and error within 100 mm. This system is also robust to occlusion and environmental changes. However, thus far, the positioning errors in an autonomous mobile robot, controlled by these systems using the SS ultrasonic signals, have not been evaluated as an experimental study. Therefore, a positioning experiment for trajectory control is conducted using an autonomous mobile robot and our positioning system. The effectiveness of this positioning method for robot self-localization is shown, from this experiment, because the average control error between the target position and the robot's position at 29 mm is obtained.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제17권4호
• /
• pp.1200-1215
• /
• 2023

With the development of IoT and artificial intelligence, location-based services are getting more and more attention. For solving the current problem that indoor positioning error is large and generalization is poor, this paper proposes a Model Stacking Algorithm for Indoor Positioning System using WiFi fingerprinting. Firstly, we adopt a model stacking method based on Bayesian optimization to predict the location of indoor targets to improve indoor localization accuracy and model generalization. Secondly, Taking the predicted position based on model stacking as the observation value of particle filter, collaborative particle filter localization based on model stacking algorithm is realized. The experimental results show that the algorithm can control the position error within 2m, which is superior to KNN, GBDT, Xgboost, LightGBM, RF. The location accuracy of the fusion particle filter algorithm is improved by 31%, and the predicted trajectory is close to the real trajectory. The algorithm can also adapt to the application scenarios with fewer wireless access points.

• 로봇학회논문지
• /
• 제2권1호
• /
• pp.40-47
• /
• 2007

Recently, with the development of service robots and with the new concept of ubiquitous world, the position estimation of mobile objects has been raised to an important problem. As pre-liminary research results, some of the localization schemes are introduced, which provide the absolute location of the moving objects subjected to large errors. To implement a precise and convenient localization system, a new absolute position estimation method for a mobile robot in indoor environment is proposed in this paper. Design and implementation of the localization system comes from the usage of active beacon systems (based upon RFID technology). The active beacon system is composed of an RFID receiver and an ultra-sonic transmitter: 1. The RFID receiver gets the synchronization signal from the mobile robot and 2. The ultra-sonic transmitter sends out the traveling signal to be used for measuring the distance. Position of a mobile robot in a three dimensional space can be calculated basically from the distance information from three beacons and the absolute position information of the beacons themselves. Since it is not easy to install the beacons at a specific position precisely, there exists a large localization error and the installation time takes long. To overcome these problems, and provide a precise and convenient localization system, a new auto calibration algorithm is developed in this paper. Also the extended Kalman filter has been adopted for improving the localization accuracy during the mobile robot navigation. The localization accuracy improvement through the proposed auto calibration algorithm and the extended Kalman filter has been demonstrated by the real experiments.

• 한국산학기술학회논문지
• /
• 제18권11호
• /
• pp.826-833
• /
• 2017

열차의 위치를 정확하게 추정하는 것은 열차제어를 위해 필수적이며 선로변에 시스템을 설치하지 않고 열차의 위치를 추정하기 많은 연구가 있었다. 열차는 선로 위를 움직이므로 위치 추정을 일차원적으로 검색이 가능하며 그 선로 또한 위치 정보를 미리 알 수 있다는 특징이 있다. 특히 고속열차의 경우 속도가 커서 도플러 주파수가 비교적 크며 선로 모양도 직선 혹은 반경이 큰 곡선으로 되어 있다. 본 연구에서는이를 활용하여 두 지점(기지국)에서 송신한 신호의 도플러 주파수를 이용하여 열차의 위치를 추정하는 방안에 대하여 제안한다. 직선과 곡선, 직곡선 혼합 선로 구간에 대하여 위치를 추정하는 수식을 도출한다. 일반적으로 도플러 레이더는 속도를 측정하는데 사용되나 여기에서는 속도와 위치를 모르는 상태에서 두 신호의 도플러 주파수 비율을 이용하여 위치와 속도를 순차적으로 구한다. 시뮬레이션을 통해 열차 위치 및 측정 오차 수준에 따른 추정 오차의 변화를 구한다. 제안하는 방안과 기존 방안에서의 목표 추정 오차 수준을 얻기 위한 조건과 측정 오차가 커짐에 따른 오차의 증가량을 비교함으로써 성능과 강인성 면에서 제안하는 방안의 우수성을 보인다.

• 한국멀티미디어학회논문지
• /
• 제17권9호
• /
• pp.1092-1097
• /
• 2014

It is difficult to perform automatic formation flight of UAV (Unmanned Aerial vehicle) when GPS (Global Positionig System) is out of order or has a system error, since the relative position estimation in the flight group is impossible in that case. In this paper, we design a relative localization system for the automatic formation flight of UAV. For this purpose, we adopt digital beam forming (DBF) to estimate the angle with the central controller of the flight group and Particle Filtering scheme to compensate the estimation error of ToA (time of arrival) method. Computer simulation results present a proper distance between the central controller and a following unit to maintain the automatic formation flight.

• 제어로봇시스템학회논문지
• /
• 제7권11호
• /
• pp.933-938
• /
• 2001

In this paper, we describe a method for the mobile robot using images of a moving object. This method combines the observed position from dead-reckoning sensors and the estimated position from the images captured by a fixed camera to localize a mobile robot. Using the a priori known path of a moving object in the world coordinates and a perspective camera model, we derive the geometric constraint equations which represent the relation between image frame coordinates for a moving object and the estimated robot`s position. Since the equations are based on the estimated position, the measurement error may exist all the time. The proposed method utilizes the error between the observed and estimated image coordinates to localize the mobile robot. The Kalman filter scheme is applied to this method. Effectiveness of the proposed method is demonstrated by the simulation.