• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.14-19
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• 2021

This paper presents LiDAR static obstacle map based vehicle dynamic state estimation algorithm for urban autonomous driving. In an autonomous driving, state estimation of host vehicle is important for accurate prediction of ego motion and perceived object. Therefore, in a situation in which noise exists in the control input of the vehicle, state estimation using sensor such as LiDAR and vision is required. However, it is difficult to obtain a measurement for the vehicle state because the recognition sensor of autonomous vehicle perceives including a dynamic object. The proposed algorithm consists of two parts. First, a Bayesian rule-based static obstacle map is constructed using continuous LiDAR point cloud input. Second, vehicle odometry during the time interval is calculated by matching the static obstacle map using Normal Distribution Transformation (NDT) method. And the velocity and yaw rate of vehicle are estimated based on the Extended Kalman Filter (EKF) using vehicle odometry as measurement. The proposed algorithm is implemented in the Linux Robot Operating System (ROS) environment, and is verified with data obtained from actual driving on urban roads. The test results show a more robust and accurate dynamic state estimation result when there is a bias in the chassis IMU sensor.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.6
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• pp.487-493
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• 2001

In this paper, a new reduced-order dynamic observer method is presented. Two types of observers are pronounced, namely, the model based reduced-order dynamic observer and the Luenburger type reduced-order dynamic observer. Useful design algorithms are also provided for each structure. The essential features of the proposed observed design methods are addressed to be qualified ad effective observers. The proposed method clarifies the duality between the controller and observer designs.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.806-811
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• 1994

Two kinds of static and dynamic state estimation methods are newly discussed for the problem of the measurement disturbance of environmental low-frequency noise in the presence of wind-induced noise. First, the probability characteristics of wind-induced noise are discussed in the form of probability distribution conditioned by wind speed, based on the simultaneous observation of the wind-induced noise and wind speed near a microphone. Next, especially form the viewpoint of simplicity for practical use, two kinds of static and dynamic state estimation methods are discussed. The static estimation method using the information on wind speed is fundamentally supported by the conservation principle of energy sum. The dynamic one is the method by using a recursive digital filter with the parameters successively renewed by the information on wind speed. This can be also simplified by using well-know Kalman filter under the assumption of the Gaussian distribution. The effectiveness of proposed two estimation methods are shown through experiments under a breezy condition in the open filed.

• Proceedings of the Korea Concrete Institute Conference
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• 2006.05a
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• pp.506-509
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• 2006

Extended Kalman Filter iterate the prediction and the filtering based on Initial state for the next time step. EKF method for the estimation of nonlinear parameters of a structural dynamic system is necessary that initial of state vector and error covariance matrix. Because those are unknown exactly, generally selected random values. That occasion observability problem appear because of unknown initial values. In this study, for the estimation of the nonlinear parameters, a simple one degree of Freedom example is carried out by Extended Kalman Filter. And initial value assumption for Parameter Estimation of Dynamic System are developed. The result of analysis is compared with calculated standard values.

• Korean Journal of Remote Sensing
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• v.7 no.1
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• pp.45-67
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• 1991

An overview is given on stochastic techniques with which remotely sensed data may be used together with ground measurements for purposes of state estimation and prediction. They can explicitly account for spatiotemporal differences in measurement characteristics between ground measurements and remotely sensed data, and are suitable for highly variant space or space-time processes, such as atmosperic processes, which may be viewed as (containing) a random process. For state estimation of static ststems, optimal linear estimation is described. As alternatives, various co-kriging estimation techniques are also described, including simple, ordinary, universal, lognormal, disjunctive, indicator, and Bayesian extersion to simple and lognormal. For illustrative purposes, very simple examples of optimal linear estimation and simple co-kriging are given. For state estimation and prediction of dynamic system, distributed-parameter kalman filter is described. Issues concerning actual implemention are given, and with application potential are described.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.1
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• pp.38-44
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• 1998

The modified maximum likelihood estimation method is used to estimate the nondimensional aerodynamic derivatives of a single turbo-prop aircraft at a specified flight condition for the best deduction of the dynamic characteristics. In wind axes the six degree of freedom equations are algebraically linearized so that the linear state equation contains aerodynamic derivatives in a state-space form and is used in the maximum likelihood method. The simulated data added with the measurement noise is used as a flight test data which is necessary to the estimation of nondimensional aerodynamic derivatives. It is obtained by implementing the 6-DOF nonlinear flight simulation. In the flight simulation, the effects of several control input types, control deflection amplitudes, and the turbulence intensities on the statistical convergence criteria are also examined and quantitative analysis of the results is discussed.

• Explosives and Blasting
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• v.37 no.1
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• pp.14-23
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• 2019

It has been an always issue for the blasting or the impact analysis to consider the strength characteristics of the rock materials associate with loading rate dependency. Due to the nature of transient loading, the dynamic rock test requires a careful technique to achieve the stress equilibrium state of the specimen. In this study, to investigate the relationship between the rock dynamic strength and the stress equilibrium state, a series of dynamic uniaxial compression tests for Pocheon granite were performed. As a result, the unbalanced stress state on the specimen can lead to the premature failure on the specimen and the less estimation of dynamic strength characteristic as well as the overestimation of strain rate. Consequently, a careful consideration of rock fracture process to achieve the dynamic force balance on the specimen should be required to make an reasonable evaluation of rock dynamic strength.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.162-165
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• 1995

For an accurate on-line measurement of the ship's attitude the paper develops an intelligent sensing system which uses one servo-type accelerometer and two servo-type inclinometers appropriately located on the ship. By considering the dynamics of the servo-controlled rigid pendulums of the inclinometers, linear equations for the rolling and pitching of the ship are derived separately from each other. Moreover, one accelerometer is used for extracting the heaving signal. Through the introduction of linear dynamic models and the linear observation equations for the heaving, rolling and pitching, the on-line measurement of the three signals can be reduced to the state estimation of the linear dynamic systems. A bank of Kalman filters is adaptively used to achieve the on-line accurate state estimation and to overcome changes in parameters in the linear dynamic models.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.2
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• pp.772-778
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• 2012

In this paper, the states and the parameters in the dynamic system are simultaneously estimated by applying the UKF(Unscented Kalman Filter), which is widely used for estimating the state of non-linear systems. Estimating the parameter is very important in various fields, such as system control, modeling, analysis of performance, and prediction. Most of the dynamic systems which are dealt with in engineering have non-linearity as well as some noise. Therefore, the parameter estimation is difficult. This paper estimates the states and the parameters applying to the UKF, which is a non-linear filter and has strong noise. The augmented equation is used by including the addition of the parameter factors to the original state equation of the system. Moreover, it is simulated by applying to a 2-DOF(Degree of Freedom) dynamic system composed of the pendulum and the slide. The measurement noise of the dynamic equation is assumed to be a Gaussian distribution. As the simulation results show, the proposed parameter estimation performs better than the LSM(Least Square Method). Furthermore, the estimation errors and convergence time are within three percent and 0.1 second, respectively. Consequentially, the UKF is able to estimate the system states and the parameters for the system, despite having measurement data with noise.

• 한국태양에너지학회:학술대회논문집
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• 2012.03a
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• pp.419-424
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• 2012

This paper discusses the state-of-the-art techniques in real-time state estimation for the Smart Microgrids. The most popular method used in traditional power system state estimation is a Weighted Least Square(WLS) algorithm which is based on Maximum Likelihood(ML) estimation under the assumption of static system state being a set of deterministic variables. In this paper, we present a survey of dynamic state estimation techniques for Smart Microgrids based on Belief Propagation (BP) when the system state is a set of stochastic variables. The measurements are often too sparse to fulfill the system observability in the distribution network of microgrids. The BP algorithm calculates posterior distributions of the state variables for real-time sparse measurements. Smart Microgrids are modeled as a factor graph suitable for characterizing the linear correlations among the state variables. The state estimator performs the BP algorithm on the factor graph based the stochastic model. The factor graph model can integrate new models for solar and wind correlation. It provides the Smart Microgrids with a way of integrating the distributed renewable energy generation. Our study on Smart Microgrid state estimation can be extended to the estimation of unbalanced three phase distribution systems as well as the optimal placement of smart meters.