• Journal of Institute of Control, Robotics and Systems
• /
• v.21 no.11
• /
• pp.1057-1063
• /
• 2015

This paper proposes a new nonlinear filtering algorithm that combines the unscented Kalman filter (UKF) and the finite impulse response (FIR) filter. The proposed filter is called the unscented Kalman/FIR hybrid filter (UKFHF). In the UKFHF algorithm, the UKF is used as the main filter, which produces state estimates under ideal conditions. When failures of the UKF are detected, the FIR filter is operated. Using the output of the FIR filter, the UKF is reset and rebooted. In this way, the UKFHF recovers from failures. The proposed UKFHF is applied to indoor human localization using wireless sensor networks. Through simulations, the performance of the UKFHF is demonstrated in comparison with that of the UKF.

• Journal of Astronomy and Space Sciences
• /
• v.28 no.3
• /
• pp.193-201
• /
• 2011

A strategy for geostationary orbit (or geostationary earth orbit [GEO]) surveillance based on optical angular observations is presented in this study. For the dynamic model, precise analytical orbit model developed by Lee et al. (1997) is used to improve computation performance and the unscented Kalman filer (UKF) is applied as a real-time filtering method. The UKF is known to perform well under highly nonlinear conditions such as surveillance in this study. The strategy that combines the analytical orbit propagation model and the UKF is tested for various conditions like different level of initial error and different level of measurement noise. The dependencies on observation interval and number of ground station are also tested. The test results shows that the GEO orbit determination based on the UKF and the analytical orbit model can be applied to GEO orbit tracking and surveillance effectively.

• Proceedings of the KIEE Conference
• /
• 2006.07d
• /
• pp.1787-1788
• /
• 2006

In this paper, a noisy passive telemetry sensor system using Unscented Kalman Filter (UKF) is proposed. To overcome these trouble problems such as a power limitation and a estimation complexity that the general passive telemetry sensor system including IC chip has, the principle of inductive coupling was applied to the modelling of a passive telemetry sensor system (PTSS) and its noisy capacitive parameter was estimated by the UKF algorithm. Specialty, to show the effective tracking performance of the UKF, we compared with the tracking performance of Recursive Least Square Estimation (RLSE) using linearization

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2011.05a
• /
• pp.107-109
• /
• 2011

본 논문은 UKF(uncented Kalman filter)를 이용한 이동체의 위치측정 정밀도 향상에 관한 연구이다. 기존에 사용된 위치측정 기술로는 유선과 마그네틱 유동 방식들이 있다. 하지만 이러한 방식들은 높은 유지 보수비용으로 인해 최근에는 레이저 내비게이션이 많이 이용되고 있다. 하지만 레이저 내비게이션은 헤더가 회전 하면서 반사체를 인식하여 위치를 계산하는 구조로써, 응답속도가 느리고 주행 속도에 따라 정밀도가 크게 떨어지는 단점이 있다. 따라서 본 논문에서는 느린 응답속도와 위치측정 오차를 해결하기 위해서 UKF를 이용한 센서융합 방법을 제안한다. 제안한 방법의 실험은 차축구동 방식의 지게차를 이용하여 레이저 내비게이션의 위치측정 결과와 비교하였다. 실험 결과, 제안된 방법이 레이저 내비게이션에 의해 계측된 위치측정 데이터보다 정밀도가 향상됨을 확인하였다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2008.04a
• /
• pp.168-173
• /
• 2008

본 연구는 UKF Algorithm을 이용한 정전용량형 원격RF센서시스템을 개발하였다. 원격 RF센서 시스템이란 wireless, implantable 그리고 batterless을 만족하는 센서 시스템을 의미한다. 기존의 원격 RF센서 시스템은 보편적으로 집적회로 타입을 채택하지만, 그 구조의 복잡성과 전력소모의 제약을 받는다. 이러한 제약을 해결하기 위해 본 연구에서는 R, L 그리고 C만으로 구성되어있는 유도결합원리를 이용한 원격 RF센서 시스템을 제안하였다. 제안된 RF 센서 시스템은 압력 혹은 습도와 같은 환경의 변화를 정전용량 값으로 측정할 수 있으며 센서의 정전 용량 값을 측정하기 위해 비선형시스템의 파라메타추정에 적합한 Unscented Kalman Filter(UKF) 기법을 채택하였다. UKF 기법을 이용하기 위해 제안된 시스템은 페이저법을 사용하여 수학적으로 모델링되었다. 마지막으로, 제안된 UKF 알고리즘을 이용한 원력 RF센서시스템이 잡음환경에서도 정전용량값을 비교적 정확하게 추정가능함을 확인하였다.

• Journal of Institute of Control, Robotics and Systems
• /
• v.18 no.3
• /
• pp.175-180
• /
• 2012

A nonlinear navigation filter for biomimetic robot using analytic approximation of mean and covariance of state variable is proposed. The approximations are performed at the time update step in the filter structure. The mean is approximated to the 3rd order of Taylor's series expansion of true mean and the covariance is approximated to the 3rd order either. The famous EKF is a nonlinear filtering method approximating the mean to 1st order and the covariance to the 3rd order. The UKF approximate them to the higher orders by numerical method. The proposed method derived a analytical approximation of them for navigation system and therefore don't need so called sigma point transformation in UKF. The simulation results show that the proposed method can be a good alternative of UKF in the systems which require less computational burden.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2012.05a
• /
• pp.45-46
• /
• 2012

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.16 no.5
• /
• pp.315-324
• /
• 2023

In this paper, we propose a real-time pose estimation method for a monocular camera using a particle filter integrated with UKF (unscented Kalman filter). While conventional camera tracking techniques combine camera images with data from additional devices such as gyroscopes and accelerometers, the proposed method aims to use only two-dimensional visual information from the camera without additional sensors. This leads to a significant simplification in the hardware configuration. The proposed approach is based on a particle filter integrated with UKF. The pose of the camera is estimated using UKF, which is defined individually for each particle. Statistics regarding the camera state are derived from all particles of the particle filter, from which the real-time camera pose information is computed. The proposed method demonstrates robust tracking, even in the case of rapid camera shakes and severe scene occlusions. The experiments show that our method remains robust even when most of the feature points in the image are obscured. In addition, we verify that when the number of particles is 35, the processing time per frame is approximately 25ms, which confirms that there are no issues with real-time processing.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.2331-2334
• /
• 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
• /
• v.29 no.10
• /
• pp.1128-1136
• /
• 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.