• Journal of the Computational Structural Engineering Institute of Korea
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• v.31 no.3
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• pp.141-145
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• 2018

The paper presents a new dynamic displacement measurement system. The developed displacement measurement system consists of a sensor module, a base module and a computation module. The sensor module, which contains a force-balanced accelerometer and low-price RTK-GNSS, measures the high-precision acceleration with sampling frequency of 100Hz, the low-precision displacement and velocity with sampling frequency of 10Hz. The measured data is transferred to the computation module through LAN cable, and precise displacement is estimated in real-time with 100Hz sampling frequency through a two stage Kalman filter. The field test was conducted at San Francisco-Oaklmand Bay bridge, CA, USA to verify the precision of the developed system, and it showed the RMSE was 1.68mm.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.2
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• pp.107-116
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• 2003

This paper presents a systematic design methodology for fault tolerant controller against a fault in actuators and sensors of linear stochastic systems with uncertainties. The scheme is based on fault detection and diagnosis(isolation and estimation) using a bank of robust two-stage Kalman filters, and accommodation of the actuator fault by eigenstructure assignment and immediate compensation of the sensor's faulty measurement. In order to clarify the fault feature in test statistics of residual, noise reduction method is given by multi-scale discrete wavelet transform. The effectiveness of our approach Is shown via simulations for a VTOL(vertical take-off and landing) aircraft subjected to parameter variations, external disturbances, process and sensor noises.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2594-2599
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• 2003

We consider some problems of the Modified SPRT(Sequential Probability Ratio Test) method for fault detection and isolation of inertial redundant sensor systems and propose an Advanced SPRT method to solve the problems of the Modified SPRT method. One problem of the Modified SPRT method to apply to inertial sensor system comes from the effect of inertial sensor errors such as misalignment, scale factor error and sensor bias in the parity vector, which make the Modified SPRT method hard to be applicable. The other problem is due to the correlation of parity vector components which may induce false alarm. We use a two-stage Kalman filter to remove effects of the inertial sensor errors and propose the modified parity vector and the controlled parity vector which removes the effect of correlation of parity vector components. The Advanced SPRT method is derived form the modified parity vector and the controlled parity vector. Some simulation results are presented to show the usefulness of the Advanced SPRT method to redundant inertial sensor systems.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.10
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• pp.747-754
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• 2003

An enhancement of the probabilistic data association filter is presented for tracking a single maneuvering target in clutter environment. The use of the variable dimensional structure leads the probabilistic data association filter to adjust to real motion of a target. The detection of the maneuver for the model switching is performed by the acceleration estimates taken from a bias estimator of the two stage Kalman filter. The proposed algorithm needs low computational power since it is implemented with a single filtering procedure. A simple Monte Carlo simulation was performed to compare the performance of the proposed algorithm and the IMMPDA filter.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.5
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• pp.39-49
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• 2017

A Space Launch Vehicle (SLV) for Launching Satellites Consists of Multi-stage Rockets for the Purpose of Efficient Flight and Accomplishes the Launch Mission through Flight Events such as Stage Separation, Engine Start and Stop. In this Process, the SLV is Supposed to Undergo the Processes of the Powered Flight Section in which the Engine Generates Thrust and the Ballistic Flight Section in which there is no Thrust Repeatedly. Because it is Difficult to Express these Flight Characteristics of the SLV as a Single Dynamics Model, much Research on Tracking Algorithms using Multiple Models has been Undertaken. In case of using the Multiple Model Tracking Algorithm, it is Expected to Improve the Tracking Performance of the SLV. However, it is Difficult to Select Proper Dynamics Models to be used and the Calculation Amount Increases due to the use of Multiple Models. In this Paper, we Propose a Method to Track the SLV with Diverse Flight Characteristics Efficiently by only Two Kalman Filters using Constant Acceleration Model and Adaptive Singer Model.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.6
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• pp.569-577
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• 2015

The navigation algorithm developed based on the extended Kalman filter (EKF) sometimes diverges when the linearity between the measurements and the states is not preserved. In this study, new update conditions together with two conditions from previous study for gravity gradient referenced navigation (GGRN) were deduced for the filter performance. Also, the effect of each update conditions was evaluated imposing the various magnitudes of the database (DB) and the sensor errors. In case the DB and the sensor errors were supposed to 0.1 Eo and 0.01 Eo, the navigation performance was improved in the eight trajectories by using part of gravity gradient components that independently estimate states located within trust boundary. When applying only the components showing larger variation, around 200% of improvement was found. Even the DB and sensor error were supposed to 3 Eo, six update conditions improved performance in at least seven trajectories. More than five trajectories generated better results with 5 Eo error of the DB and the sensor. Especially, two update conditions successfully control divergence, and bounded the navigation error to the 1/10 level. However, these update conditions could not be generalized for all trajectories so that it is recommended to apply update conditions at the stage of planning, or as an index of precision of GGRN when combine with various types of geophysical data and algorithm.

• Journal of Broadcast Engineering
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• v.14 no.6
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• pp.692-702
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• 2009

Real-time virtual studios which could run only on expensive workstations are now available for personal computers thanks to the recent development of graphics hardware. Nevertheless, graphics are rendered off-line in the post production stage in film or TV drama productions, because the graphics' quality is still restricted by the real-time hardware. Software-based camera tracking methods taking only the source video into account take much computation time, and often shows unstable results. To overcome this restriction, we propose a system that stores camera motion data from sensors at shooting time as common virtual studios and uses them in the post production stage, named as POVIS(post virtual imaging system). For seamless registration of graphics onto the camera video, precise zoom lens calibration must precede the post production. A practical method using only two planar patterns is used in this work. We present a method to reduce the camera sensor's error due to the mechanical mismatch, using the Kalman filter. POVIS was successfully used to track the camera in a documentary production and saved much of the processing time, while conventional methods failed due to lack of features to track.

• Journal of Sensor Science and Technology
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• v.28 no.5
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• pp.334-339
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• 2019

In this study, we propose a compensation method of raw LiDAR data with noise and noise filtering for signal processing of LiDAR sensors during the development phase. The raw LiDAR data include constant errors generated by delays in transmitting and receiving signals, which can be resolved by LiDAR signal compensation. The signal compensation consists of two stage. First one is LiDAR sensor calibration for a compensation of geometric distortion. Second is walk error compensation. LiDAR data also include fluctuation and outlier noise, the latter of which is removed by data filtering. In this study, we compensate for the fluctuation by using the Kalman filter method, and we remove the outlier noise by applying a Gaussian weight function.