• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2331-2334
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• 2003

The basic difference between the EKF(Extended Kalman Filter) and UKF(Unscented Kalman Filter) stems from the manner in which Gaussian random variables(GRV) are represented for propagating through system dynamics. In the EKF, the state distribution is approximated by a GRV, which is then propagated analytically through the first-order linearization of the nonlinear system. This can possibly introduce large errors in the true posterior mean and covariance of the transformed GRV, which may lead to sub-optimal performance and sometimes divergence of the filter. However, the UKF addresses this problem by using a deterministic sampling approach. The state distribution is also approximated by a GRV, but is now represented using a minimal set of carefully chosen sample points. These sample points completely capture the true mean and covariance of the GRV, and UKF captures the posterior mean and covariance accurately up to the 2nd order(Taylor series expansion) for any nonlinearity. This paper utilizes the UKF to determine spacecraft orbit when only magnetometer is available. Several catastrophic failures of spacecraft in orbit have been attributed to failures of the spacecraft mission. Recently studies on contingency-major sensor failure cases- have been performed. For mission success, contingency design or plan should be implemented in case of a major sensor failure. Therefore the algorithm presented in this paper can be used for a spacecraft without GPS or contingency design in case of GPS failure.

• Journal of the Korean Society for Precision Engineering
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• v.29 no.10
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• pp.1128-1136
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• 2012

In this paper, the states and parameters in a dynamic system are estimated by applying an Unscented Kalman Filter (UKF). The UKF is widely used in various fields such as sensor fusion, trajectory estimation, and learning of Neural Network weights. These estimations are necessary and important in determining the stability of a mobile system, monitoring, and predictions. However, conventional approaches are difficult to estimate based on the experimental data, due to properties of non-linearity and measurement noises. Therefore, in this paper, UKF is applied in estimating the states and parameters needed. An experimental dynamic system has been set up for obtaining data and the experimental data is collected for parameter estimation. The measurement noises are primarily reduced by applying the Low Pass Filter (LPF). Given the simulation results, the estimated error rate is 39 percent more efficient than the results obtained using the Least Square Method (LSM). Secondly, the estimated parameters have an average convergence period of four seconds.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.3
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• pp.221-227
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• 2010

Recent development in seeker technology explores a new seeker design in which, with larger field-of-view (FOV), optical parts are strapped down to a body (hence, called as a body-fixed seeker or a strapdown seeker). This design has several advantages such as comparatively easier maintenance and calibration by removing complex mechanical moving parts, increasing reliability, and cost savings. On the other hand, the strapdown seeker involves difficulties in implementing guidance laws since it does not directly provide inertial LOS rates. Instead, information for generating guidance commands should be extracted by estimating missile/target relative motion utilizing target images on the image plane of a strapdown seeker. In this research, a new framework based on an unscented Kalman filter is developed for estimating missile/target relative motion on the simplified assumption of a point source target. Performance of a terminal guidance algorithm, in which guidance command is generated based on the estimated relative motion, is demonstrated by a missile/target engagement simulation.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.191-196
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• 2006

In GPS Positioning, the error of satellite orbit will affect user's position accuracy directly, it is important to determine the satellite orbit precise. The real-time orbit is needed in kinematic GPS positioning, the precise GPS orbit from IGS would be delayed long time, so orbit prediction is key to real-time kinematic positioning. We analyze the GPS predicted ephemeris, on the base of comparison of EKF and UKF, a new orbit prediction method is put forward based on UKF in this paper, the result shows that UKF improves the orbit predicted precision and stability. It offers a new method for others satellites orbit determination as Galileo, and so on.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.12
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• pp.1865-1874
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• 2013

In this paper, a design method of speed and position estimator based on unscented Kalman filter is proposed for the no sensor control of IPMSM(Interior Permanent Magnet Synchronous Motor). The proposed method is simple more than the estimator designed with rotation axis for current measurement. Also the proposed state estimator is designed including nonlinear terms of the estimator. The controller which constructed using nonlinear back-stepping control method is operated speed and current control using the estimated speed and currents information. Through simulation, the performance of the designed estimator is compared to the estimator which is designed to synchronize d-q axis.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.8
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• pp.752-759
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• 2008

In this paper, we propose a high accurate geo-location system based on a single base station, where its location is obtained by Time-of-Arrival(ToA) and Direction-of-Arrival(DoA) of the radio signal. For estimating accurate ToA and DoA information, a MUltiple SIgnal Classification(MUSIC) is adopted. However, the estimation of ToA and DoA using MUSIC algorithm is a time-consuming process. The position tracking bias is occurred by the time delay caused by the estimation process. In order to mitigate the bias error, we propose the estimation method of the position tracking bias and compensate the location error produced by the time delay using the position tracking bias mitigation. For accurate self-localization of mobile robot, the Unscented Kalman Filter(UKF) with position tracking bias is applied. The simulation results show the efficiency and accuracy of the proposed geo-location system and the enhanced performance when the Unscented Kalman Filter is adopted for mobile robot application.

• Journal of Aerospace System Engineering
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• v.11 no.4
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• pp.22-29
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• 2017

A study is performed for the effective detection method of a fault which is occurred during operation in a small turbojet engine with non-linear characteristics used by unmanned air vehicle. For this study the non-linear dynamic model of the engine is derived from transient thermodynamic cycle analysis. Also for inducing real operation conditions the controller is developed associated with unscented Kalman filter to estimate noises. Sequential probability ratio test is introduced as a real time method to detect a fault which is manipulated for simulation as a malfunction of rotational speed sensor contaminated by large amount of noise. The method applied to the fault detection during operation verifies its effectiveness and high feasibility by showing good and definite decision performances of the fault.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.1
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• pp.280-294
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• 2023

Unmanned aerial vehicles (UAVs) and millimeter-wave frequencies play key roles in supporting 5G wireless communication systems. They expand the field of wireless communication by increasing the data capacities of communication systems and supporting high data rates. However, short wavelengths, owing to the high millimeter-wave frequencies can cause problems, such as signal attenuation and path loss. To address these limitations, research on high directional beamforming technologies continue to garner interest. Furthermore, owing to the mobility of the UAVs, it is essential to track the beam angle accurately to obtain full beamforming gain. This study presents a beam tracking method based on the unscented Kalman filter using hybrid beamforming. The simulation results reveal that the proposed beam tracking scheme improves the overall performance in terms of the mean-squared error and spectral efficiency. In addition, by expanding analog beamforming to hybrid beamforming, the proposed algorithm can be used even in multi-user and multi-stream environments to increase data capacity, thereby increasing utilization in new-radio multiple-input multiple-output orthogonal frequency-division multiplexing systems.

• Smart Structures and Systems
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• v.16 no.2
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• pp.295-328
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• 2015

In recent years the monitoring of structural behavior through acquisition of vibrational data has become common practice. In addition, recent advances in sensor development have made the collection of diverse dynamic information feasible. Other than the commonly collected acceleration information, Global Position System (GPS) receivers and non-contact, optical techniques have also allowed for the synchronous collection of highly accurate displacement data. The fusion of this heterogeneous information is crucial for the successful monitoring and control of structural systems especially when aiming at real-time estimation. This task is not a straightforward one as measurements are inevitably corrupted with some percentage of noise, often leading to imprecise estimation. Quite commonly, the presence of noise in acceleration signals results in drifting estimates of displacement states, as a result of numerical integration. In this study, a new approach based on a time domain identification method, namely the Unscented Kalman Filter (UKF), is proposed for correcting the "drift effect" in displacement or rotation estimates in an online manner, i.e., on the fly as data is attained. The method relies on the introduction of artificial white noise (WN) observations into the filter equations, which is shown to achieve an online correction of the drift issue, thus yielding highly accurate motion data. The proposed approach is demonstrated for two cases; firstly, the illustrative example of a single degree of freedom linear oscillator is examined, where availability of acceleration measurements is exclusively assumed. Secondly, a field inspired implementation is presented for the torsional identification of a tall tower structure, where acceleration measurements are obtained at a high sampling rate and non-collocated GPS displacement measurements are assumed available at a lower sampling rate. A multi-rate Kalman Filter is incorporated into the analysis in order to successfully fuse data sampled at different rates.

• Journal of Astronomy and Space Sciences
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• v.26 no.1
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• pp.31-46
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• 2009

An Unscented Kalman Filter (UKF) for estimation of the attitude and rate of a spacecraft using only magnetometer vector measurement is developed. The attitude dynamics used in the estimation is the nonlinear Euler's rotational equation which is augmented with the quaternion kinematics to construct a process model. The filter is designed for small satellite in low Earth orbit, so the disturbance torques include gravity-gradient torque, magnetic disturbance torque, and aerodynamic drag torque. The magnetometer measurements are simulated based on time-varying position of the spacecraft. The filter has been tested not only in the standby mode but also in the detumbling mode. Two types of actuators have been modeled and applied in the simulation. The PD controller is used for the two types of actuators (reaction wheels and thrusters) to detumble the spacecraft. The estimation error converged to within 5 deg for attitude and 0.1 deg/s for rate respectively when the two types of actuators were used. A joint state parameter estimation has been tested and the effect of the process noise covariance on the parameter estimation has been indicated. Also, Monte-Carlo simulations have been performed to test the capability of the filter to converge with the initial conditions sampled from a uniform distribution. Finally, the UKF performance has been compared to that of the EKF and it demonstrates that UKF slightly outperforms EKF. The developed algorithm can be applied to any type of small satellites that are actuated by magnetic torquers, reaction wheels or thrusters with a capability of magnetometer vector measurements for attitude and rate estimation.