• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.61.2-61
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• 2001

The purpose of the initial alignment of a SDINS is to get a coordinate transformation matrix from the body frame to the navigation frame. The initial alignment is one of the most important processes in the navigation system since its error has a large influence on the navigation solution. In this paper, a real-time initial alignment algorithm for the SDINS is developed using the Kalman filter. The steady state error analysis is performed for the developed Kalman filter technique and the gyrocompass loop method. The performance of the developed alignment method is compared with the gyrocompass loop method through the real-time alignment experiments.

• 한국기계가공학회지
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• 제15권2호
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• pp.131-138
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• 2016

In order to improve the performance of an extended Kalman filter, a simplified indirect inference method (SIIM) fuzzy logic system (FLS) is proposed. The proposed FLS is composed of two fuzzy input variables, four fuzzy rules and one fuzzy output. Two normalized fuzzy input variables are the variance between the trace of a prior and a posterior covariance matrix, and the residual error of a Kalman algorithm. One fuzzy output variable is the weighting factor to adjust for the Kalman gain. There is no need to decide the number and the membership function of input variables, because we employ the normalized monotone increasing/decreasing function. The single parameter to be determined is the magnitude of a universe of discourse in the output variable. The structure of the proposed FLS is simple and easy to apply to various nonlinear state estimation problems. The simulation results show that the proposed FLS has strong adaptability to estimate the states of the incoming/outgoing moving objects, and outperforms the conventional extended Kalman filter algorithm by providing solutions that are more accurate.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제13권2호
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• pp.133-139
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• 2013

This paper proposes a vision-based indoor localization method for autonomous vehicles. A single upward-facing digital camera was mounted on an autonomous vehicle and used as a vision sensor to identify artificial landmarks and any natural corner features. An interest point detector was used to find the natural features. Using an optical flow detection algorithm, information related to the direction and vehicle translation was defined. This information was used to track the vehicle movements. Random noise related to uneven light disrupted the calculation of the vehicle translation. Thus, to estimate the vehicle translation, a Kalman filter was used to calculate the vehicle position. These algorithms were tested on a vehicle in a real environment. The image processing method could recognize the landmarks precisely, while the Kalman filter algorithm could estimate the vehicle's position accurately. The experimental results confirmed that the proposed approaches can be implemented in practical situations.

• Smart Structures and Systems
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• 제22권5호
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• pp.631-641
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• 2018

This paper outlines a computational procedure for the effective merging of diverse sensor measurements, displacement and acceleration signals in particular, in order to successfully monitor and simulate the current health condition of civil structures under dynamic loadings. In particular, it investigates a Kalman Filter implementation for the Heterogeneous Data Fusion of displacement and acceleration response signals of a structural system toward dynamic identification purposes. The procedure is perspectively aimed at enhancing extensive remote displacement measurements (commonly affected by high noise), by possibly integrating them with a few standard acceleration measurements (considered instead as noise-free or corrupted by slight noise only). Within the data fusion analysis, a Kalman Filter algorithm is implemented and its effectiveness in improving noise-corrupted displacement measurements is investigated. The performance of the filter is assessed based on the RMS error between the original (noise-free, numerically-determined) displacement signal and the Kalman Filter displacement estimate, and on the structural modal parameters (natural frequencies) that can be extracted from displacement signals, refined through the combined use of displacement and acceleration recordings, through inverse analysis algorithms for output-only modal dynamics identification, based on displacements.

• Smart Structures and Systems
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• 제22권4호
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• pp.399-411
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• 2018

The difficulty in modeling complex nonlinear structures lies in the presence of significant sources of uncertainties mainly attributed to sudden changes in the structure's behavior caused by regular aging factors or extreme events. Quantifying these uncertainties and accurately representing them within the complex mathematical framework of Structural Health Monitoring (SHM) are significantly essential for system identification and damage detection purposes. This study highlights the importance of uncertainty quantification in SHM frameworks, and presents a comparative analysis between intrusive and non-intrusive techniques in quantifying uncertainties for SHM purposes through two different variations of the Kalman Filter (KF) method, the Ensemble Kalman filter (EnKF) and the Polynomial Chaos Kalman Filter (PCKF). The comparative analysis is based on a numerical example that consists of a four degrees-of-freedom (DOF) system, comprising Bouc-Wen hysteretic behavior and subjected to El-Centro earthquake excitation. The comparison is based on the ability of each technique to quantify the different sources of uncertainty for SHM purposes and to accurately approximate the system state and parameters when compared to the true state with the least computational burden. While the results show that both filters are able to locate the damage in space and time and to accurately estimate the system responses and unknown parameters, the computational cost of PCKF is shown to be less than that of EnKF for a similar level of numerical accuracy.

• 전자공학회논문지S
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• 제36S권7호
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• pp.34-42
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• 1999

다중표적 추적시스템은 여러 개의 표적물을 동시에 추적한다. 표적물의 추적에는 일반적으로 칼만필터를 사용하게 된다. 칼만필터는 최적의 특성을 지니고 있지만, 많은 계산량을 요구하는 단점이 있다. 따라서 여러 개의 표적물을 동시에 추적하는 다중표적 추적시스템의 실시간 구현을 위하여 칼만필터 대신에 계산량이 적은 다른 예측기를 사용하기도 한다. 본 논문에서는 계산량을 줄이기 위하여 칼만필터에서 사용하는 시스템의 모델을 줄이는 방법을 사용하여 보았다. 표적물의 운동을 등속운동으로 가정하여 사용된 모델은 표적물의 추적능력을 지니면서도 그 계산량을 줄일 수 있었다. 간단한 시뮬레이션과 실제의 영상정보에 적용한 결과는 등속운동을 가정한 칼만필터가 원래의 좋은 특성을 유지하면서 계산량을 줄일 수 있어 다중표적 추적시스템에 유리하게 사용될 수 있음을 보여주었다.

• 제어로봇시스템학회논문지
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• 제7권4호
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• pp.363-363
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• 2001

We study on the velocity matching algorithm for transfer alignment of inertial navigation system(INS) using a robust H₂ filter. We suggest an uncertainty model and a discrete robust H₂filter for INS and apply the suggested robust H₂ filter to the uncertainty model. The discrete robust H₂filter is shown by simulation to have better performance time and accuracy than Kalman filter.

• Journal of Positioning, Navigation, and Timing
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• 제4권4호
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• pp.181-186
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• 2015

A High-degree Cubature Kalman Filter (CKF) is proposed to deal with the Strapdown Inertial Navigation System (SDINS) alignment problem. In-flight Alignment (IFA) is an effective method to compensate for attitude errors of the navigation system. While providing precise attitude error compensation, however, the external source aided alignment often creates a nonlinear filtering problem caused by a large misalignment angle. Introduced recently, Cubature Kalman Filter is a suitable technique for various nonlinear problems. In this paper, a higher degree CKF is applied to this accuracy-is-everything SDINS IFA problem. The simulation results show that the proposed technique outperformed a traditional nonlinear filter in terms of precision and alignment time.

• 한국산업융합학회 논문집
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• 제22권3호
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• pp.265-272
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• 2019

For tracking a varying number of people using laser range finder, it is important to deal with appearance/disappearance of people due to various causes including occlusions. We propose a method for tracking people with automatic initialization by integrating observations from laser range finder. In our method, the problem of estimating 2D positions and orientations of multiple people's walking direction is formulated based on a mixture kalman filter. Proposal distributions of a kalman filter are constructed by using a mixture model that incorporates information from a laser range scanner. Our experimental results demonstrate the effectiveness and robustness of our method.

• Journal of International Society for Simulation Surgery
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• 제2권1호
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• pp.17-25
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• 2015

Purpose In this paper, we propose a robust 3D vessel tracking algorithm by utilizing an active contour model and unscented Kalman filter which are the two representative algorithms on segmentation and tracking. Materials and Methods The proposed algorithm firstly accepts user input to produce an initial estimate of vessel boundary segmentation. On each Computed Tomography Angiography (CTA) slice, the active contour is applied to segment the vessel boundary. After that, the estimation process of the unscented Kalman filter is applied to track the vessel boundary of the current slice to estimate the inter-slice vessel position translation and shape deformation. Finally both active contour and unscented Kalman filter are inter-operated for vessel segmentation of the next slice. Results The arbitrarily shaped blood vessel boundary on each slice is segmented by using the active contour model, and the Kalman filter is employed to track the translation and shape deformation between CTA slices. The proposed algorithm is applied to the 3D visualization of chest CTA images using graphics hardware. Conclusion Through this algorithm, more opportunities, giving quick and brief diagnosis, could be provided for the radiologist before detailed diagnosis using 2D CTA slices, Also, for the surgeon, the algorithm could be used for surgical planning, simulation, navigation and rehearsal, and is expected to be applied to highly valuable applications for more accurate 3D vessel tracking and rendering.