• 자동차안전학회지
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• 제14권4호
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• pp.16-20
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• 2022

Accurate state estimation is important for autonomous driving. However, the estimation error increases in situations that a lot of longitudinal slip occurs. Therefore, this paper presents a vehicle state estimation method using an Extended Kalman Filter. The filter estimates the states of the host vehicle robust to wheel slip. It utilizes the measurements of the four-wheel rotational speeds, longitudinal acceleration, yaw-rate, and steering wheel angle. Nonlinear measurement model is represented by Ackermann Model. The main advantage of this approach is the accurate estimation of yaw rate due to the measurement of the steering wheel angle. The proposed algorithm is verified in scenarios of autonomous emergency braking (AEB), lane change (LC), lane keeping (LK) using an automated vehicle. The results show that the proposed algorithm guarantees accurate estimation in such scenarios.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.1822-1825
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• 2003

A practical range estimator based on the robust Kalman filter is proposed to solve the range estimation problem for surface to air missile(SAM) homing guidance. Apart from the previous works based on the extended Kalman filter(EKF) with bearing only measurement, the proposed scheme makes use of line-of-sight(LOS) rate to ensure the fast convergency at long-range. In this reason, the robust Kalman filter is considered to deal with LOS rate measurement error. The recursive linear structure of proposed filter is easy to implement and make it possible to reduce computational burdens. Moreover, it shows good estimation performance without specific guidance law such as oscillation proportional navigation guidance(OPNG).

• International Journal of Aeronautical and Space Sciences
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• 제18권3호
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• pp.535-544
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• 2017

In-flight aircraft engine performance estimation is one of the key techniques for advanced intelligent engine control and in-flight fault detection, isolation and accommodation. This paper detailed the current performance degradation estimation methods, and an improved hybrid Kalman filter via velocity-based LPV (VLPV) framework for these needs is proposed in this paper. Composed of a nonlinear on-board model (NOBM) and VLPV, the filter shows a hybrid architecture. The outputs of NOBM are used for the baseline of the VLPV Kalman filter, while the system performance degradation factors on-line estimated by the measured real system output deviations are fed back to the NOBM for its updating. In addition, the setting of the process and measurement noise covariance matrices' values are also discussed. By applying it to a commercial turbofan engine, simulation results show the efficiency.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2012년도 추계학술대회 논문집
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• pp.215-216
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• 2012

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.860-863
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• 1996

In this paper, we formulate the extended Kalman filter for calibration of gimballed inertial navigation system (GINS) at a pure navigation mode with 1500 ft/sec initial velocity and compare its performance to the linear Kalman filter's by using Monte-Carlo analysis method. It has been shown that estimation performance of the extended Kalman filter is better than that of the linear Kalman filter.

• 항공우주시스템공학회지
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• 제10권1호
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• pp.15-20
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• 2016

The optimal estimation of a general continuous-discrete system can be achieved through the solution of the Fokker-Planck equation and the Bayesian update. Due the high nonlinearity of the equation of motion of the system and the measurement model, it is necessary to linearize the both equation. To avoid linearization, the filter based on Fokker-Planck equation is designed. with the unscented transformation update mechanism, in which the associated Fokker-Planck equation was solved efficiently and accurately via discrete quadrature and the measurement update was done through the unscented transformation update mechanism. This filter based on the Direct Quadrature Moment of Method(DQMOM) and the unscented transformation update is applied to the bearing only target tracking problem. The proposed filter can still provide more accurate estimation of the state than those of the extended Kalman filter especially when measurements are sparse. Simulation results indicate that the advantages of the proposed filter based on the DQMOM and the unscented transformation update make it a promising alternative to the extended Kalman filter.

• Journal of information and communication convergence engineering
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• 제8권3호
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• pp.267-272
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• 2010

For the last decade, recurrent neural networks (RNNs) have been commonly applied to communications channel equalization. The major problems of gradient-based learning techniques, employed to train recurrent neural networks are slow convergence rates and long training sequences. In high-speed communications system, short training symbols and fast convergence speed are essentially required. In this paper, the derivative-free Kalman filter, so called the unscented Kalman filter (UKF), for training a fully connected RNN is presented in a state-space formulation of the system. The main features of the proposed recurrent neural equalizer are fast convergence speed and good performance using relatively short training symbols without the derivative computation. Through experiments of nonlinear channel equalization, the performance of the RNN with a derivative-free Kalman filter is evaluated.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2009년도 정보 및 제어 심포지움 논문집
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• pp.187-189
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• 2009

Position estimation in indoor is significant problem, because GPS which is usually used for outdoor positioning cannot be utilized to indoor positioning. Sensor network can be a solution for the positioning. Recently, chirp spread spectrum(CSS) specified in IEEE 802.15.4a provides an ability of ranging. Based on the results of the ranging, a position of a CSS node can be calculated by using trilateration. In this case, Kalman filter can be applied to the trilateration because of the measurement noise. In this paper, we apply the unscented Kalman filter for the trilateration. The trilateration can be represented to a nonlinear state space equation, and the unscented Kalman filter is suitable for nonlinear state space equation. Through the experimental results. we show the accuracy of the estimated position.

• Journal of Astronomy and Space Sciences
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• 제22권4호
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• pp.501-512
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• 2005

본 논문에서는 우주개발 중장기계획에 따라 개발 중인 KSLV-I 우주발사체가 나로 우주센터에서 발사될 경우를 가정하여 확장칼만필터와 Unscented 칼만필터를 통한 실시간 궤적추정 결과를 비교 분석하였다. 이를 위해 가상의 KSLV-I과 관측 레이더 3기를 바탕으로 실제 궤적을 생성하였으며, 관측자료는 실제 궤적에 관측오차를 고려하여 생성하였다. 이에 대해 초기 추정오차가 작은 경우와 큰 경우로 구분하고, 관측주기가 20Hz와 10Hz인 경우로 나누어 각각 두 필터를 적용해서 성능을 비교하였다. Unscented 칼만필터는 확장칼만필터보다 큰 초기 추정오차에 대해 수렴이 빠르고 정확도가 높으며, 느린 관측주기에도 우수한 성능을 보이는 것을 확인하였다.

• International Journal of Control, Automation, and Systems
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• 제1권4호
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• pp.444-452
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• 2003

An integration system with multi-measurement sets can be realized via combined application of a centralized and federated Kalman filter. It is difficult for the centralized Kalman filter to remove a failed sensor in comparison with the federated Kalman filter. All varieties of Kalman filters monitor innovation sequence (residual) for detection and isolation of a failed sensor. The innovation sequence, which is selected as an indicator of real time estimation error plays an important role in adaptive mechanism design. In this study, the centralized Kalman filter with adaptive measurement fusion is introduced by means of innovation sequence. The objectives of adaptive measurement fusion are automatic isolation and recovery of some sensor failures as well as inherent monitoring capability. The proposed adaptive filter is applied to the GPS/SDINS integration system with an additional sensor. Simulation studies attest that the proposed adaptive scheme is effective for isolation and recovery of immediate sensor failures.