• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
• /
• pp.148-148
• /
• 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.

• Journal of Information Display
• /
• 제3권2호
• /
• pp.19-25
• /
• 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.

• 반도체디스플레이기술학회지
• /
• 제22권2호
• /
• pp.112-119
• /
• 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.

• 한국생산제조학회지
• /
• 제23권5호
• /
• pp.485-497
• /
• 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.

• 스마트미디어저널
• /
• 제5권1호
• /
• pp.88-94
• /
• 2016

본 논문에서는 희소성 표현을 기반으로 하는 객체 추적 방법에 있어서 객체 표류 현상을 처리하기 위한 새로운 방법을 제시한다. 그중에서도 APG-L1 (accelerated proximal gradient L1) 방법은 희소성 표현이란 객체의 외형을 표현하기 위한 목표 템플릿(target template)과 배경이나 폐색(occlusion)과 같은 객체 이외의 부분을 대체하기 위한 기본 템플릿(trivial template)를 이용하여 입력 영상을 표현하는 방법이다. 또한 어파인 변환행렬을 이용한 particle filtering 이 적용되어 객체의 위치를 찾고 APG 방법을 사용하여 희소성기반의 L1-norm을 최소화한다. 본 논문에서는 객체추적의 표류현상을 방지하기 위하여 기본 템플릿의 계수를 활용하여 배경을 가진 객체가 채택되는 현상을 방지하는 방법을 제시한다. 다양한 영상에 적용하여 제안하는 방법을 실험한 결과, 기존의 방법들과 비교하여 높은 성과를 보인다.

• 전자공학회논문지SC
• /
• 제39권2호
• /
• pp.176-181
• /
• 2002

이 논문에서는 PDA(Probabilistic Data Association) 필터를 이용한 표적 추적에서 평균자승추정오차의 기대값을 최소화하는 표적 검출 문턱값의 최적제어 방법을 제시하고, 닫힌 형태의 준최적해를 구한다. 최적의 검출 문턱값을 구하기 위한 이전의 연구에서는 그래프를 이용한 부정확한 최적화 방법이나, 매우 시간이 많이 소요하는 수치해석적 최적화 알고리듬을 사용하였다. 하지만, 이 논문에서는 정보감소인자의 수치적 근사화식을 이용하여, 최적제어문제로 정식화하여 닫힌 형태의 준최적 검출 문턱값을 구하였다. 이 결과는 실시간 표적 추적에서 유용하게 사용될 수 있다.

• 한국기계가공학회지
• /
• 제10권4호
• /
• pp.102-108
• /
• 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.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1999년도 제14차 학술회의논문집
• /
• pp.181-184
• /
• 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.

• 한국음향학회지
• /
• 제18권6호
• /
• pp.9-16
• /
• 1999

이 논문은 측정잡음이 큰 수동소나체계의 각도정보만을 이용한 표적기동분석(TMA ; Target Motion Analysis) 기법 연구의 일환으로서 표적의 상태변수를 추정하는 순차적 추정자로 수정이득확장칼만필터(MGEKF : Modified Gain Extended Kalman Filter)를 사용하며, 이 MGEKF의 초기화를 위해 비선형 batch estimation 알고리듬을 제안한다. 수동표적추적 시스템의 가관측성(observability) 해석을 바탕으로 시스템의 가관측성의 향상을 통해 TMA 성능을 개선시킬 수 있는 관측자의 기동을 결정하는 실용적이면서도 효과적인 방법을 제안한다. 또한 가관측성 확보가 어려운 초기단계의 TMA를 위해 관측자의 진행방향과 표적의 각도정보와 같은 기하학적 자료와 시스템의 가관측성과의 관계를 나타내는 engagement boundary를 산출하여 가관측성이 큰 기하학적 관계를 갖는 위치로 관측자를 선기동(pre-maneuver) 시키는 방법도 제시한다. 제시하는 TMA 기법의 성능을 시뮬레이션을 통해 입증한다.

• Journal of information and communication convergence engineering
• /
• 제10권4호
• /
• pp.390-395
• /
• 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.