• Journal of the Korean Society for Aviation and Aeronautics
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• v.24 no.2
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• pp.1-10
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• 2016

A GPS/SDINS/ST(Star Tracker) integrated sensor algorithm is more robust than the GPS/SDINS and the ST/SDINS systems on exploration of other planets. Most of the advanced studies shown that GPS/SDINS/ST integrated sensor with centralized Kalman filter was more accurate than those 2 integrated systems. The system, however, consist of a single filter, it is vulnerable to defects on failed data. To improve the problem, we work out a study using federated Kalman filter(No-Reset mode) and centralized Kalman filter with adaptive measurement fusion which known as robustness on fault. The simulation results show that the debasing influences are reduced and the computation is enable at least 100Hz. Further researches that the initial calibration in accordance with observability and applying the exploration trajectory are needed.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.3
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• pp.283-289
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• 2015

In this paper, a GNSS interference detection algorithm based on an adaptive fading Kalman filter is proposed to detect a spoofing signal which is one of the threatening GNSS intentional interferences. To detect and mitigate the spoofing signal, the fading factor of the filter is used as a detection parameter. For simulation, the effect of the spoofing signal is modeled by the ramp type bias error of the pseudorange to emulate a smart spoofer and the change of the fading factor value according to ramp type bias error is quantitatively analyzed. In addition, the detection threshold is established to detect the spoofing signal by analyzing the change of the error covariance and the effect of spoofing is mitigated by controlling the Kalman gain of the filter. To verify the performance analysis of the proposed algorithm, various simulations are implemented. Through the results of simulations, we confirmed that the proposed algorithm works well.

• International Journal of Control, Automation, and Systems
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• v.2 no.3
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• pp.310-318
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• 2004

In this paper, an unscented Kalman filter (UKF) for curvilinear motions in an interacting multiple model (IMM) algorithm to track a maneuvering vehicle on a road is investigated. Driving patterns of vehicles on a road are modeled as stochastic hybrid systems. In order to track the maneuvering vehicles, two kinematic models are derived: A constant velocity model for linear motions and a constant-speed turn model for curvilinear motions. For the constant-speed turn model, an UKF is used because of the drawbacks of the extended Kalman filter in nonlinear systems. The suggested algorithm reduces the root mean squares error for linear motions and rapidly detects possible turning motions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.4
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• pp.986-991
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• 2014

The PPG signal used in mobile-healthcare and telemedicine system is including the various motion artifact that is signal generated from patient's movements. Recently, although the various methods to remove motion artifacts have been suggested, the performances of these methods are still not satisfactory. Therefore, this s study suggested the novel method based on the Kalman filter and adaptive filter to remove motion artifacts, and we used various motion artifacts to analyze the performance of the proposed method. In the results of experiments, the signal-to-noise ratio of proposed method showed good performace that was 4.8 times of moving average filter.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.16 no.28
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• pp.93-101
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• 1993

In this paper, two adaptive exponentially weighted moving avenge control chart schemes which available for real-time are proposed. The weighting coefficient is estimated using a recursive kalman filter algorithm. Simulated average run lengths indicate the proposed schemes are sensitive to process shifts And their performance is comparable to CUSUM control chart and customary EWMA control chart.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.6
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• pp.108-116
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• 2008

In this paper, we propose an algorithm that can estimate the distance using disparity based on stereo vision system, even though the obstacle is located in long ranges as well as short ranges. We use sub-pixel interpolation to minimize quantization errors which deteriorate the distance accuracy when calculating the distance with integer disparity, and also we use enhanced adaptive fuzzy strong tracking Kalman filter(EAFSTKF) to improve the distance accuracy and track the path optimally. The proposed method can solve the divergence problem caused by nonlinear dynamics such as various vehicle movements in the conventional Kalman filter(CKF), and also enhance the distance accuracy and reliability. Our simulation results show that the performance of our method improves by about 13.5% compared to other methods in point of root mean square error rate(RMSER).

• Journal of Korean Society of Water and Wastewater
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• v.20 no.6
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• pp.829-836
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• 2006

The reaction rate of denitrification is primarily affected by the utilization of organics that are usually limited in the anoxic period in a sequencing batch reactor. It is necessary to add an extemal carbon source for sufficient denitrification. An adaptive model of state-space based on the extended Kalman filter is applied to manipulate the dosage rate of extemal carbon automatically. Control strategies for denitrification have been studied to improve control performance through simulations. The normal control strategy of the constant set-point results in the overdosage of external carbon and deterioration of water quality. To prevent the overdosage of external carbon, improved control strategies such as the constrained control action, variable set-point, and variable set-point after dissolved oxygen depletion are required. More stable control is obtained through the application of the variable set-point after dissolved oxygen depletion. The converging value of the estimated denitrification coefficient reflects conditions in the reactor.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.1
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• pp.287-304
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• 2012

Spectrum sensing is an important functionality for cognitive users to look for spectrum holes before taking transmission in dynamic spectrum access model. Unlike previous works that assume perfect knowledge of the SNR of the signal received from the primary user, in this paper we consider a realistic case where the SNR of the primary user's signal is unknown to both fusion center and cognitive radio terminals. A Kalman filter based adaptive Takagi and Sugeno's fuzzy system is designed to make the global spectrum sensing decision based on the observed energies from cognitive users. With the capacity of adapting system parameters, the fusion center can make a global sensing decision reliably without any requirement of channel state information, prior knowledge and prior probabilities of the primary user's signal. Numerical results prove that the sensing performance of the proposed scheme outperforms the performance of the equal gain combination based scheme, and matches the performance of the optimal soft combination scheme.

• The KIPS Transactions:PartD
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• v.17D no.3
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• pp.185-192
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• 2010

In sensor network environments, each sensor measures the physical environments according to the sampling period, and transmits a sensor reading to the base station. Thus, the sample period influences against importance resources such as a network bandwidth, and a battery power. In this paper, we propose new adaptive sampling technique that adjusts the sampling period of a sensor with respect to the features of sensor readings. The proposed technique predicts a future readings based on KF (Kalman Filter). By using the differences of actual readings and estimated reading, we identify the importance of sensor readings, and then, we adjust the sampling period according to the importance. In our experiments, we demonstrate the effectiveness of our technique.

• Journal of the Society of Naval Architects of Korea
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• v.45 no.3
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• pp.269-279
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• 2008

In this paper, an underwater navigation system with adaptive receding horizon Kalman filter (ARHKF) is studied. It is well known that incorrect statistical information and temporal disturbance invoke errors of any navigation systems with Kalman filter, which makes the autonomous navigation difficult in real underwater environment. In this context, two kinds of problems are herein considered. The first one is the development of an algorithm, which estimates the noise covariance of a linear discrete time-varying stochastic system. The second one is the implementation of ARHKF to underwater navigation systems. The performance of the derived estimation algorithm of noise covariance and the ARHKF are verified by simulation and experiment in the towing tank of Seoul National University.