The damage detection method using the extended Kalman filter(EKF) technique has been continuously used since EKF can estimation the responses of the damaged building structure and the stiffness of the structure. However, in the use of EKF, the requirement of setting the initial paramters P, Q, and R has caused the divergence and instability of the state vector, and various researches have been conducted to determine theses parameters. In this paper, adaptive extended Kalman filter(AEKF) method is proposed to solve the problem of setting the values of P, Q, and R, which are important parameters determining the convergence performance of the EKF state vector. By using the AEKF method proposed in this study, the P, Q, and R parameters are updated every k steps. The proposed algorithm is applied for the estimation of stiffness and the damage detection of 3-DOF problem. Based of the verification, it can be found that the selection process for the values of P, Q, and R can improve the convergence performance of EKF.

The key motivation of this study is for a style of the sensor arrangement to have an effect on the localization performance of mobile robots in case of using sonar sensors. In general robot platforms with sonar sensors, sonar sensors are supposed to be radially arranged on their rotational axis of mobile robots. However, relevant limits to several functions required for their autonomous navigation occur unexpectedly, because a sonar sensor generally has the negative nature of its wide beam width together with the specular reflection. We present a new strategy of the sonar sensor arrangement capable of enhancing the localization performance. Sonar sensors are intended to be arranged nonradially (twistedly expressed in this paper) on their rotational axis. The localization scheme called STARER: Sonar-Twisted ARrangement localizER is based on the extended Kalman filter (EKF) with occupancy grid maps. Experimental results demonstrate the validity and robustness of the proposed method for the localization of mobile robots.

PURPOSES: This study is to predict the Sound Pressure Level(SPL) obtained from the Noble Close ProXimity(NCPX) method by using the Extended Kalman Filter Algorithm employing the taylor series and Linear Regression Analysis based on the least square method. The objective of utilizing EKF Algorithm is to consider stochastically the effect of error because the Regression analysis is not the method for the statical approach. METHODS: For measuring the friction noise between the surface and vehicle’s tire, NCPX method was used. With NCPX method, SPL can be obtained using the frequency analysis such as Discrete Fourier Transform(DFT), Fast Fourier Transform(FFT) and Constant Percentage Bandwidth(CPB) Analysis. In this research, CPB analysis was only conducted for deriving A-weighted SPL from the sound power level in terms of frequencies. EKF Algorithm and Regression analysis were performed for estimating the SPL regarding the vehicle velocities. RESULTS : The study has shown that the results related to the coefficient of determination and RMSE from EKF Algorithm have been improved by comparing to Regression analysis. CONCLUSIONS : The more the vehicle is fast, the more the SPL must be high. But in the results of EKF Algorithm, SPLs are irregular. The reason of that is the EKF algorithm can be reflected by the error covariance from the measurements.

In this paper, a study of 2-step damage detection for space truss structures using the extended Kalman filter theory is presented. Space truss structures are composed of many members, so it is difficult to find damaged member from the whole system. Therefore, 2-step damage identification method is applied to detect the damaged members. First, kinetic energy change ratio is used to find damage region including damaged member and then detect damaged member using extended Kalman filtering algorithm in damage region. The effectiveness of proposed method is verified through the numerical examples.

Recently, as the awareness of safety has become more important, studies on damage assessment techniques for building structures have been actively conducted. The damage of the building structure is caused by the decrease of the stiffness which is inherent dynamic characteristic of the structural system, and the decrease of stiffness acts as a direct variable connected to the collapse of the structure. there have been developed techniques for estimating the inherent dynamics of a structure to identify and evaluate damage to the structure. In this study, we estimate the layer mass due to the modeling error through the optimization algorithm, Genetic Algorithm, and use the optimization algorithm GA to optimize the error covariance matrix, system noise and measured noise covariance matrix We propose an optimal state estimation algorithm. The objective function of the GA algorithm is obtained by the residual which is the difference between the measured values obtained from the EKF calculation and the values obtained from the system model. We verified the feasibility of the algorithm through a 4-DOF system.

Scouring of bridge foundation is one of the major cause of bridge failure. Scour can be defined as the excavation of foundation or other material from the bed and banks of streams, due to water flow. Scour monitoring is one of the major requirements to ensure bridge safety. There are some underwater instruments such as float-out devices which are used in scour monitoring. The available conventional underwater instruments are expensive and difficulties in maintenance. Thus vibration based monitoring techniques are emerging, this paper is one of such effort. This paper develops a vibration-based scour monitoring technique. The effect of sour on the vibration characteristics of pier is not significant at the early stages of scouring but significant changes in vibration characteristic can be identified during moderate level of scour. Thus this method can be used to identify and alert the safety of bridge prior its failure. An Extended Kalman filter is employed in this process. This paper numerically validates the monitoring capability of developed method over other vibrations based methods.

본 연구에서는 Z-R 관계식의 매개변수를 안정적인 값으로 실시간 예측하고자 확장 칼만 필터기법을 적용하였다. 이를 위해 Z-R 관계식의 비선형을 고려하여 확장 칼만 필터로 매개변수 결정모형을 구축하였다. 상태-공간 모형은 Adamowski and Muir(1989)의 연구를 기반으로 구축하였다. 상태-공간 모형의 상태변수는 Z-R 관계식의 두 매개변수로 설정하였다. 결과적으로 칼만이득과 상태변수가 발산하지 않는 안정적인 모형을 구축하였다. 주목할 점으로는 기존 방법으로 추정된 과대 혹은 과소한 매개변수가 필터링 되어 일부 제거되었다는 것이다. 부적절한 매개변수의 적용은 물리적으로 비현실적인 강우강도 추정 결과를 불러일으키는 원인이기 때문에 이러한 결과는 정량적 강수량 추정측면에서 효과가 크다고 할 수 있다. 또한 확장 칼만 필터로 예측한 매개변수로 레이더 강우를 추정한 결과, 편의보정계수가 1.0에 근사하게 나타나 편의보정과정 없이도 지상 강우강도와의 평균적인 차이는 근소한 것으로 나타났다. 또한 기존 방법으로 레이더 강우를 추정한 결과보다 전반적으로 정확도 높은 강우 추정이 가능한 것으로 나타났다.

본 연구에서는 자료동화기법의 하나인 확장 칼만 필터를 이용하여 유량자료의 실시간 품질향상을 수행하였다. 확장 칼만 필터의 상태-공간모형은 강우-유출모형과 관측유량자료를 이용하여 구성하였다. 본 연구에서는 실시간 품질향상 목적을 댐 유입량의 비정상적 고변동성 억제 및 관측유량의 결‧오측 보완으로 구분하였으며, 각각의 경우에 적절한 확장 칼만 필터 모형을 제시하였다. 이들 모형의 차이는 칼만이득 계산에 필요한 공분산 함수의 추정에 변동성만을 고려하는냐 또는 편의까지를 포함하느냐로 나타난다. 본 연구는 충주댐 유역을 대상으로 적용하였으며, 그 결과 제시된 모형들이 댐 유입량자료나 결‧오측이 포함된 유량자료의 실시간 품질향상에 효과적으로 작동함을 확인하였다.

This paper describes a method of road tracking by using a vision and laser with extracting road boundary (road lane and curb) for navigation of intelligent transport robot in structured road environments. Road boundary information plays a major role in developing such intelligent robot. For global navigation, we use a global positioning system achieved by means of a global planner and local navigation accomplished with recognizing road lane and curb which is road boundary on the road and estimating the location of lane and curb from the current robot with EKF(Extended Kalman Filter) algorithm in the road assumed that it has prior information. The complete system has been tested on the electronic vehicles which is equipped with cameras, lasers, GPS. Experimental results are presented to demonstrate the effectiveness of the combined laser and vision system by our approach for detecting the curb of road and lane boundary detection.

This paper proposes a robust image stabilization system for a mobile robot using an Extended Kalman Filter (EKF). Though image information is one of the most efficient data used for robot navigation, it is subjected to noise which is the result of internal vibration as well as external factors such as uneven terrain, stairs, or marshy surfaces. The camera vibration deteriorates the image resolution by destroying the image sharpness, which seriously prevents mobile robots from recognizing their environment for navigation. In this paper, an inclinometer was used to measure the vibration angle of the camera system mounted on the robot to obtain a reliable image by compensating for the angle of the camera vibration. In addition the angle prediction obtained by using the EKF enhances the image response analysis for real time performance. The experimental results show the effectiveness of the proposed system used to compensate for the blurring of the images.