• International Journal of Aeronautical and Space Sciences
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• v.12 no.1
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• pp.24-36
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• 2011

A new approach for spacecraft absolute attitude estimation based on the unscented Kalman filter (UKF) is extended to relative attitude estimation and navigation. This approach for nonlinear systems has faster convergence than the approach based on the standard extended Kalman filter (EKF) even with inaccurate initial conditions in attitude estimation and navigation problems. The filter formulation employs measurements obtained from a vision sensor to provide multiple line(-) of(-) sight vectors from the spacecraft to another spacecraft. The line-of-sight measurements are coupled with gyro measurements and dynamic models in an UKF to determine relative attitude, position and gyro biases. A vector of generalized Rodrigues parameters is used to represent the local error-quaternion between two spacecraft. A multiplicative quaternion-error approach is derived from the local error-quaternion, which guarantees the maintenance of quaternion unit constraint in the filter. The scenario for bounded relative motion is selected to verify this extended application of the UKF. Simulation results show that the UKF is more robust than the EKF under realistic initial attitude and navigation error conditions.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.4
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• pp.341-348
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• 2013

A location estimation algorithm based on the sea-surface beacon is proposed in this paper. The beacon is utilized to provide ultrasonic signals to the underwater vehicles around the beacon to estimate precise position of underwater vehicles (ROV, AUV, Diver robot), which is named as USBL (Ultra Short Baseline) system. It utilizes GPS and INS data for estimating its position and adopts DWT (Discrete Wavelet Transform) de-noising filter and UKF (Unscented KALMAN Filter) elaborating the position estimation. The beacon system aims at estimating the precise position of underwater vehicle by using USBL to receive the tracking signals. The most important one for the precise position estimation of underwater vehicle is estimating the position of the beacon system precisely. Since the beacon is on the sea-waves, the received GPS signals are noisy and unstable most of times. Therefore, the INS data (gyroscope sensor, accelerometer, magnetic compass) are obtained at the beacon on the sea-surface to compensate for the inaccuracy of the GPS data. The noises in the acceleration data from INS data are reduced by using DWT de-noising filter in this research. Finally the UKF localization system is proposed in this paper and the system performance is verified by real experiments.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.4
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• pp.468-473
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• 2014

This paper describes the in-flight alignment of SDINS (Strapdown Inertial Navigation Systems) using an EKF (Extended Kalman Filter) and a UKF (Unscented Kalam Filter), which allow large initial attitude error uncertainty. Regardless of the inertial sensors, there are nonlinear error dynamics of SDINS in cases of large initial attitude errors. A UKF that is one of the nonlinear filtering approaches for IFA (In-Flight Alignment) are used to estimate the attitude errors. Even though the EKF linearized model makes velocity errors when predicting incorrectly in case of large attitude errors, a UKF can represent correctly the velocity errors variations of attitude errors with nonlinear attitude error components. Simulation results and analyses show that a UKF works well to handle large initial attitude errors of SDINS and the alignment error attitude estimation performance are quite improved.

• Journal of Astronomy and Space Sciences
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• v.35 no.3
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• pp.163-173
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• 2018

This paper presents relative navigation using intermittent laser-based measurement data for spacecraft flying formation that consist of two spacecrafts; namely, chief and deputy spacecrafts. The measurement data consists of the relative distance measured by a femtosecond laser, and the relative angles between the two spacecrafts. The filtering algorithms used for the relative navigation are the extended Kalman filter (EKF), unscented Kalman filter (UKF), and least squares recursive filter (LSRF). Numerical simulations reveal that the relative navigation performances of the EKF- and UKF-based relative navigation algorithms decrease in accuracy as the measurement outage period increases. However, the relative navigation performance of the UKF-based algorithm is 95 % more accurate than that of the EKF-based algorithm when the measurement outage period is 80 sec. Although the relative navigation performance of the LSRF-based relative navigation algorithm is 94 % and 370 % less accurate than those of the EKF- and UKF-based navigation algorithms, respectively, when the measurement outage period is 5 sec; the navigation error varies within a range of 4 %, even though the measurement outage period is increased. The results of this study can be applied to the design of a relative navigation strategy using the developed algorithms with laser-based measurements for spacecraft formation flying.

• International Journal of Control, Automation, and Systems
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• v.5 no.4
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• pp.388-396
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• 2007

This paper describes a multipath estimation method for Global Positioning System (GPS) dual frequency carrier phase measurements. Multipath is a major error source in high precision GPS applications, i.e., carrier phase measurements for precise positioning and attitude determinations. In order to estimate and remove multipath at carrier phase measurements, an array GPS antenna system has been used. The known geometry between the antennas is used to estimate multipath parameters. Dual frequency carrier phase measurements increase the redundancy of measurements, so it can reduce the number of antennas. The unscented Kalman filter (UKF) is recently applied to many areas to overcome some of the limitations of the extended Kalman filter (EKF) such as weakness to severe nonlinearity. This paper uses the UKF for estimating multipath parameters. A series of simulations were performed with GPS antenna arrays located on a straight line with one reflector. The geometry information of the antenna array reduces the number of estimated multipath parameters from four to three. Both the EKF and the UKF are used as estimation algorithms and the results of the EKF and the UKF are compared. When the initial parameters are far from true parameters, the UKF shows better performance than the EKF.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.4
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• pp.361-368
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• 2015

This paper reports an unscented Kalman filter approach for localization of a mobile robot in an indoor environment. The method proposes a new model of measurement uncertainty which adjusts the error covariance according to the measured distance. The method also uses non-zero off diagonal values in error covariance matrices of motion uncertainty and measurement uncertainty. The method is tested through experiments in an indoor environment of 100*40 m working space using a differential drive robot which uses Laser range finder as an exteroceptive sensor. The results compare the localization performance of the proposed method with the conventional method which doesn't use adaptive measurement uncertainty model. Also, the experiment verifies the improvement due to non-zero off diagonal elements in covariance matrices. This paper contributes to implementing and evaluating a practical UKF approach for mobile robot localization.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.5
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• pp.416-422
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• 2013

This paper suggests a hybrid path planning method of multi-robots, where a path deviation prevention for maintaining a specific formation is implemented by using repulsive function, $A^*$ algorithm and UKF (Unscented Kalman Filter). The repulsive function in potential field method is used to avoid collision among robots and obstacles. $A^*$ algorithm helps the robots to find optimal path. In addition, error estimation based on UKF guarantees small path deviation of each robot during navigation. The simulation results show that the swarm robots with designated formation successfully avoid obstacles and return to the assigned formation effectively.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2001.04a
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• pp.180-189
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• 2001

In this study, recently developed unscented Kalman filter (UKF) technique is studied for identification of nonlinear structural dynamic systems as an alternative to the extended Kalman filter (EKF). The EKF, which was originally developed as a state estimator for nonlinear systems, has been frequently employed for parameter identification by introducing the state vector augmented with the unknown parameters to be identified. However, the EKF has several drawbacks such as biased estimations and erroneous estimations especially for highly nonlinear dynamic systems due to its crude linearization scheme. To overcome the weak points of the EKF, the UKF was recently developed as a state estimator. Numerical simulation studies have been carried out on nonlinear SDOF system and nonlinear MDOF system. The results from a series of numerical simulations indicate that the UKF is superior to the EKF in the system identification of nonlinear dynamic systems especially highly nonlinear systems.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2002.09a
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• pp.213-220
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• 2002

본 연구에서는 최근에 개발된 Unscented Particle Filter (UPF)를 사용한 비선형 동적 구조계의 구조계수 규명기법이 연구되었다. 일반적인 비선형 구조계수 추정 문제의 일반 해는 존재하지 않으나, 그에 대한 대안으로써 선형 근사 기법인 extended Kalman filter (EKF)가 비선형 동적 구조계수의 추정에 주로 사용되어왔다. 그러나, EKF는 구간 선형(piecewise linear) 가정으로 인해 biased estimator이고 비선형성이 상대적으로 높을 때 오차가 큰 추정치를 주는 단점을 가진다. 이를 보완하기 위해서 UPF가 개발되었고, 이 기법은 particle filter의 일종으로써 Unscented Kalman filter (UKF)를 사용하여 importance proposal distribution을 생성한다. 수치실험이 SDOF와 MDOF에 대하여 3가지 경우에 대해서 수행되었다. 비선형 SDOF의 수치 실험으로부터 잡음이 가해진 상태에서 UKF가 EKF에 비해 초기 공분산 행렬의 가정에 대해 정확하고 강인한 추정결과를 보여줌을 보였다 최하층의 column에 비선형 거동이 발생하는 5층 전단 빌딩모형의 수치실험으로부터 UKF가 복잡한 구조물의 구조계수 추정능력이 있음을 보여주었다. 여러 가지 수치실험은 UPF가 EKF보다 비선형 동적 구조계수 추정에 있어서 더 나은 방법임을 보여 주었다.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2001.10a
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• pp.117-126
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• 2001

In this study, the recently developed unscented Kalman filter (UKF) technique is studied for identification of nonlinear structural dynamic systems as an alternative to the extended Kalman filter (EKF). The EKF, which was originally developed as a state estimator for nonlinear systems, has been frequently employed for parameter identification by introducing the state vector augmented with the unknown parameters to be identified. However, the EKF has several drawbacks such as biased estimations and erroneous estimations especially for highly nonlinear dynamic systems due to its crude linearization scheme. To overcome the weak points of the EKF, the UKF was recently developed as a state estimator. Numerical simulation studies have been carried out on nonlinear SDOF system and nonlinear MDOF system. The results from a series of numerical simulations indicate that the UKF is superior to the EKF in the system identification of nonlinear dynamic systems especially highly nonlinear systems.