• Title/Summary/Keyword: Target tracking filter

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A Study of Target Motion Analysis For a Passive Sonar System with the IMM (IMM을 이용한 수동소나체계의 기동표적추적기법 향상 연구)

  • 유필훈;송택렬
    • 제어로봇시스템학회:학술대회논문집
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    • 2000.10a
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    • pp.148-148
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    • 2000
  • In this paper the IMM(Interacting Multiple model) algorithm using the MGEKF(Modified Gain Extended Kalman Filter) which modes are variances of the process noises is proposed to enhance the performance of maneuvering target tracking with bearing and frequency measurements. The state are composed of relative position, relative velocity, relative acceleration and doppler frequency. The mode probability is calculated from the bearing and frequency measurements. The proposed algorithm is tested a series of computer simulation runs.

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Convergence Control of Moving Object using Opto-Digital Algorithm in the 3D Robot Vision System

  • Ko, Jung-Hwan;Kim, Eun-Soo
    • Journal of Information Display
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    • v.3 no.2
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    • pp.19-25
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    • 2002
  • In this paper, a new target extraction algorithm is proposed, in which the coordinates of target are obtained adaptively by using the difference image information and the optical BPEJTC(binary phase extraction joint transform correlator) with which the target object can be segmented from the input image and background noises are removed in the stereo vision system. First, the proposed algorithm extracts the target object by removing the background noises through the difference image information of the sequential left images and then controlls the pan/tilt and convergence angle of the stereo camera by using the coordinates of the target position obtained from the optical BPEJTC between the extracted target image and the input image. From some experimental results, it is found that the proposed algorithm can extract the target object from the input image with background noises and then, effectively track the target object in real time. Finally, a possibility of implementation of the adaptive stereo object tracking system by using the proposed algorithm is also suggested.

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 on the Robot Vision Control Schemes of N-R and EKF Methods for Tracking the Moving Targets (이동 타겟 추적을 위한 N-R과 EKF방법의 로봇비젼제어기법에 관한 연구)

  • Hong, Sung-Mun;Jang, Wan-Shik;Kim, Jae-Meung
    • Journal of the Korean Society of Manufacturing Technology Engineers
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    • v.23 no.5
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    • pp.485-497
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    • 2014
  • This paper presents the robot vision control schemes based on the Newton-Raphson (N-R) and the Extended Kalman Filter (EKF) methods for the tracking of moving targets. The vision system model used in this study involves the six camera parameters. The difference is that refers to the uncertainty of the camera's orientation and focal length, and refers to the unknown relative position between the camera and the robot. Both N-R and EKF methods are employed towards the estimation of the six camera parameters. Based on the these six parameters estimated using three cameras, the robot's joint angles are computed with respect to the moving targets, using both N-R and EKF methods. The two robot vision control schemes are tested by tracking the moving target experimentally. Given the experimental results, the two robot control schemes are compared in order to evaluate their strengths and weaknesses.

Drift Handling in Object Tracking by Sparse Representations (희소성 표현 기반 객체 추적에서의 표류 처리)

  • Yeo, JungYeon;Lee, Guee Sang
    • Smart Media Journal
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    • v.5 no.1
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    • pp.88-94
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    • 2016
  • In this paper, we proposed a new object tracking algorithm based on sparse representation to handle the drifting problem. In APG-L1(accelerated proximal gradient) tracking, the sparse representation is applied to model the appearance of object using linear combination of target templates and trivial templates with proper coefficients. Also, the particle filter based on affine transformation matrix is applied to find the location of object and APG method is used to minimize the l1-norm of sparse representation. In this paper, we make use of the trivial template coefficients actively to block the drifting problem. We experiment the various videos with diverse challenges and the result shows better performance than others.

(Suboptimal Detection Thresholds for Tracking in Clutter) (클러터 환경에서의 표적 추적을 위한 준최적의 검출 문턱값)

  • Jeong, Yeong-Heon;Sin, Han-Seop
    • Journal of the Institute of Electronics Engineers of Korea SC
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    • v.39 no.2
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    • pp.176-181
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    • 2002
  • In this paper, we consider the optimal control of detection threshold to minimize the conditional expectation of mean-square state estimation error for a probabilistic data association (PDA) filter. Earlier works on this problem involved the cumbersome graphical optimization algorithm or time-consuming numerical optimization algorithm. Using the numerical approximation of information reduction factor, we obtained the suboptimal detection threshold in a closed-form. This results are very useful for real- time implementation.

Motion Control of the Precise Stage using Piezoelectric Actuator (압전소자를 이용한 정밀 스테이지의 운동제어)

  • Kim, In-Soo;Kim, Yeung-Shik;Hwang, Yun-Sik
    • Journal of the Korean Society of Manufacturing Process Engineers
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    • v.10 no.4
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    • pp.102-108
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    • 2011
  • LQG/LTR control scheme is applied to the two axes stage using piezoelectric actuator for tracking reference input and suppressing hysteresis effect in this paper. The plant is combined with an integrator to improve the tracking ability. LQG/LTR controller is designed by making desirable target filter loop remove all poles except for an integrator included in new design plant model and loop transfer recovery. Decoupler in the shape of FIR filter is added to remove the coupling effect between the two axes motion and so feedback control loop is designed independently for the each axis motion.

Leading Vehicle State Estimator for Adaptive Cruise Control and Vehicle Tracking

  • Lee, Choon-Young;Lee, Ju-Jang
    • 제어로봇시스템학회:학술대회논문집
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    • 1999.10a
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    • pp.181-184
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    • 1999
  • Leading vehicle states are useful and essential elements in adaptive cruise control (ACC) system, collision warning (CW) and collision avoidance (CA) system, and automated highway system (AHS). There are many approaches in ACC using Kalman filter. Mostly only distance to leading vehicle and velocity difference are estimated and used for the above systems. Applications in road vehicle in curved road need to obtain more informations such as yaw angle, steering angle which can be estimated using vision system. Since vision system is not robust to environment change, we used Kalman filter to estimate distance, velocity, yaw angle, and steering angle. Application to active tracking of target vehicle is shown.

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Target Motion Analysis for a Passive Sonar System with Observability Enhancing (가관측성 향상을 통한 수동소나체계의 표적기동 분석)

  • 한태곤;송택렬
    • The Journal of the Acoustical Society of Korea
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    • v.18 no.6
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    • pp.9-16
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    • 1999
  • As a part of target motion analysis(TMA) with highly noisy bearings-only measurements from a passive sonar system, a nonlinear batch estimator is proposed to provide the initial estimates to a sequential estimator called the modified gain extended Kalman filter(MGEKF). Based on the system observability analysis of passive target tracking, a practical and effective method is suggested to determine the observer maneuvers for improved TMA performance through system observability enhancing. Also suggested is a method to determine observer location for enhanced system observability at the initial phase of TMA from various engagement boundaries which represent the relationship between observer-target relative geometrical data and system observability. The proposed TMA methods are tested by a series of computer simulation runs.

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Automatic Mutual Localization of Swarm Robot Using a Particle Filter

  • Lee, Yang-Weon
    • Journal of information and communication convergence engineering
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    • v.10 no.4
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    • pp.390-395
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    • 2012
  • This paper describes an implementation of automatic mutual localization of swarm robots using a particle filter. Each robot determines the location of the other robots using wireless sensors. The measured data will be used for determination of the movement method of the robot itself. It also affects the other robots' self-arrangement into formations such as circles and lines. We discuss the problem of a circle formation enclosing a target that moves. This method is the solution for enclosing an invader in a circle formation based on mutual localization of the multi-robot without infrastructure. We use trilateration, which does require knowing the value of the coordinates of the reference points. Therefore, specifying the enclosure point based on the number of robots and their relative positions in the coordinate system. A particle filter is used to improve the accuracy of the robot's location. The particle filter is operates better for mutual location of robots than any other estimation algorithms. Through the experiments, we show that the proposed scheme is stable and works well in real environments.