• Title/Summary/Keyword: Target Position Estimation

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A study on the real-time Position measurements of mobile object using neural network (신경 회로망을 이용한 이동물체의 실시간 위치측정에 대한 연구)

  • Ro, Jae-H.;Yi, Un-K.;Ro, Young-S.
    • Proceedings of the KIEE Conference
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    • 1999.07b
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    • pp.832-834
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    • 1999
  • This paper is a study on the real-position measurements of mobile object using n network. 2-D PSD sensor is used to measure th position of moving object with light source. Position Sensitive Detector(PSD) is an useful which can be used to measure the position o incidence light in accuracy and in real-time. T the position of light source of moving target, neural network technique are proposed and applied. Real-time position measurements of the mobile robot with light source is examined to validate the proposed method. It is shown that the proposed technique provides accurate position estimation of the moving object.

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Matched-target Model Inversion for the Position Estimation of Moving Targets (정합-표적모델 역산을 이용한 기동 표적의 위치 추정)

  • 장덕홍;박홍배;김성일;류존하;김광태
    • The Journal of the Acoustical Society of Korea
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    • v.22 no.7
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    • pp.562-572
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    • 2003
  • A matched-target model inversion method was developed for a passive sonar to estimate the position of moving targets. Based on the well known matched-field processing in underwater acoustics, the method finds target position by matching the measured target directions and frequencies with the corresponding values of the proposed target model. For the efficient and accurate estimations, the parameter searching was accomplished using a hybrid optimizing method, which first starts with a global optimization such as generic algorithm or simulated annealing then applies a local optimization of a simple down hill algorithm. The suggested method was testified using simulations for three different moving scenarios. The simulation results showed that the method is robust in convergence, even under the situation of over 5 times standard deviation of Gaussian distribution of measured error, and is practical in calculation time as well.

Stereo Vision-Based 3D Pose Estimation of Product Labels for Bin Picking (빈피킹을 위한 스테레오 비전 기반의 제품 라벨의 3차원 자세 추정)

  • Udaya, Wijenayake;Choi, Sung-In;Park, Soon-Yong
    • Journal of Institute of Control, Robotics and Systems
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    • v.22 no.1
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    • pp.8-16
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    • 2016
  • In the field of computer vision and robotics, bin picking is an important application area in which object pose estimation is necessary. Different approaches, such as 2D feature tracking and 3D surface reconstruction, have been introduced to estimate the object pose accurately. We propose a new approach where we can use both 2D image features and 3D surface information to identify the target object and estimate its pose accurately. First, we introduce a label detection technique using Maximally Stable Extremal Regions (MSERs) where the label detection results are used to identify the target objects separately. Then, the 2D image features on the detected label areas are utilized to generate 3D surface information. Finally, we calculate the 3D position and the orientation of the target objects using the information of the 3D surface.

Position error estimation of sub-array in passive ranging sonar based on a genetic algorithm (유전자 알고리즘 기반의 수동측거소나 부배열 위치오차 추정)

  • Eom, Min-Jeong;Kim, Do-Young;Park, Gyu-Tae;Shin, Kee-Cheol;Oh, Se-Hyun
    • The Journal of the Acoustical Society of Korea
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    • v.38 no.6
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    • pp.630-636
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    • 2019
  • Passive Ranging Sonar (PRS) is a type of passive sonar consisting of three sub-array on the port and starboard, and has a characteristic of detecting a target and calculating a bearing and a distance. The bearing and distance calculation requires physical sub-array position information, and the bearing and distance accuracy performance are deteriorated when the position information of the sub-array is inaccurate. In particular, it has a greater impact on distance accuracy performance using plus value of two time-delay than a bearing using average value of two time-delay. In order to improve this, a study on sub-array position error estimation and error compensation is needed. In this paper, We estimate the sub-array position error based on enetic algorithm, an optimization search technique, and propose a method to improve the performance of distance accuracy by compensating the time delay error caused by the position error. In addition, we will verify the proposed algorithm and its performance using the sea-going data.

A Study on the GPS Error Compensation using Estimation Point of Moving Position at a Vehicle

  • Song, Suck-Woo;Song, Hyun-Sung;Jang, Hong-Seok;Rho, Do-Hwan
    • 제어로봇시스템학회:학술대회논문집
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    • 2001.10a
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    • pp.64.5-64
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    • 2001
  • It is a very important problem that we grasp the accurate position at car navigation system. The GPS has used for knowing position because of accumulating few errors, but it have errors that are Tropospheric error, ionospheric error and Multipath error and so on. In this paper, We estimate moving position of a vehicle by Kalman filter using initial value after deducing the line equation using initial value and target value of map data. Then, we compensate GPS errors compare estimated poing with GPS errors. The experimental results have shown that are compared position data during real travel with compensated position data which are got after applying the algorithm ...

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A Study on Enhancing Outdoor Pedestrian Positioning Accuracy Using Smartphone and Double-Stacked Particle Filter (스마트폰과 Double-Stacked 파티클 필터를 이용한 실외 보행자 위치 추정 정확도 개선에 관한 연구)

  • Kwangjae Sung
    • Journal of the Semiconductor & Display Technology
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    • v.22 no.2
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    • pp.112-119
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    • 2023
  • In urban environments, signals of Global Positioning System (GPS) can be blocked and reflected by tall buildings, large vehicles, and complex components of road network. Therefore, the performance of the positioning system using the GPS module in urban areas can be degraded due to the loss of GPS signals necessary for the position estimation. To deal with this issue, various localization schemes using inertial measurement unit (IMU) sensors, such as gyroscope and accelerometer, and Bayesian filters, such as Kalman filter (KF) and particle filter (PF), have been designed to enhance the performance of the GPS-based positioning system. Among Bayesian filters, the PF has been widely used for the target tracking and vehicle navigation, since it can provide superior performance in estimating the state of a dynamic system under nonlinear/non-Gaussian circumstance. This paper presents a positioning system that uses the double-stacked particle filter (DSPF) as well as the accelerometer, gyroscope, and GPS receiver on the smartphone to provide higher pedestrian positioning accuracy in urban environments. The DSPF employs a nonparametric technique (Parzen-window) to create the multimodal target distribution that approximates the posterior distribution. Experimental results show that the DSPF-based positioning system can provide the significant improvement of the pedestrian position estimation in urban environments.

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A Study of Optimization of α-β-γ-η Filter for Tracking a High Dynamic Target

  • Pan, Bao-Feng;Njonjo, Anne Wanjiru;Jeong, Tae-Gweon
    • Journal of Navigation and Port Research
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    • v.41 no.5
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    • pp.297-302
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    • 2017
  • The tracking filter plays a key role in accurate estimation and prediction of maneuvering the vessel's position and velocity. Different methods are used for tracking. However, the most commonly used method is the Kalman filter and its modifications. The ${\alpha}-{\beta}-{\gamma}$ filter is one of the special cases of the general solution provided by the Kalman filter. It is a third order filter that computes the smoothed estimates of position, velocity, and acceleration for the nth observation, and predicts the next position and velocity. Although found to track a maneuvering target with good accuracy than the constant velocity ${\alpha}-{\beta}$ filter, the ${\alpha}-{\beta}-{\gamma}$ filter does not perform impressively under high maneuvers, such as when the target is undergoing changing accelerations. This study aims to track a highly maneuvering target experiencing jerky motions due to changing accelerations. The ${\alpha}-{\beta}-{\gamma}$ filter is extended to include the fourth state that is, constant jerk to correct the sudden change of acceleration to improve the filter's performance. Results obtained from simulations of the input model of the target dynamics under consideration indicate an improvement in performance of the jerky model, ${\alpha}-{\beta}-{\gamma}-{\eta}$ algorithm as compared to the constant acceleration model, ${\alpha}-{\beta}-{\gamma}$ in terms of error reduction and stability of the filter during target maneuver.

A Study on Optimization of Fourth-Order Fading Memory Filter under the Highly Dynamic Motion of Both Own Ship and Target

  • Pan, Bao-Feng;Jeong, Tae-Gweon
    • Proceedings of the Korean Institute of Navigation and Port Research Conference
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    • 2017.11a
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    • pp.145-147
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    • 2017
  • Tracking filter plays a key role in accurate estimation and prediction of maneuvering vessel's dynamics. The third-order ${\alpha}-{\beta}-{\gamma}$ filter is one of the special cases of the general solution provided by the Kalman filter. Fading memory algorithm performs a better performance in numerous of ${\alpha}-{\beta}-{\gamma}$ filter algorithms. This study aims to optimize the fourth-order fading memory algorithm ${\alpha}-{\beta}-{\gamma}-{\eta}$ filter, which is extended form ${\alpha}-{\beta}-{\gamma}$ filter, to get much more accurate position of high dynamic target on the condition that the own ship is also high dynamic.

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Improvement of Target Motion Analysis for a Passive Sonar System with Measurement Bias Estimation (측정각 Bias 보상을 통한 수동소나체계의 표적기동분석 성능 향상 연구)

  • Yoo, Phil-Hoon;Song, Taek-Lyul
    • Proceedings of the KIEE Conference
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    • 2001.07d
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    • pp.2011-2013
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    • 2001
  • In this paper the MMAE(Multiple Model Adaptive Estimation) algorithm using the MGEKF(Modified Gain Extended Kalman Filter) of which modes are set to be measurement biases is proposed to enhance the performance of target tracking with bearing only measurements. The state are composed of relative position, relative velocity and taregt acceleration. The mode probability is calculated from the bearing only measurements from the HMS(Hull-Mounted Sonar). The proposed algorithm is tested in a series of computer simulation runs.

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Precision Position Estimation for Tracking the Moving Object (이동물체의 추적을 위한 정밀 위치추정)

  • In, Chu-Sik;Lee, Ja-Sung;Hong, Suk-Kyo;Koh, Young-Gil
    • Proceedings of the KIEE Conference
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    • 1994.11a
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    • pp.335-337
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    • 1994
  • The correlation tracker developed by John M. Fitts in 1979 is the most complex to mechanize but provides the best tracking performance in a low SNR condition. Correlation tracker would rewove the requirements for optimizing threshold and has no need to know information about the target. But if the displacement of the target is large, the tracking error of the correlation tracker tends to diverge. In this paper, we suggest a precision image tracking algorithm which improves the tracking performance via iterative application of the matched filter estimation algorithm.

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