• International Journal of Ocean System Engineering
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• v.1 no.2
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• pp.89-94
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• 2011

Underwater localization is a crucial capability for reliable operation of various types of underwater vehicles including submarines and underwater robots. However, sea water is almost impermeable to high-frequency electromagnetic waves, and thus absolute position fixes from Global Positioning System (GPS) are not available in the water. The use of acoustic telemetry systems such as Long Baseline (LBL) is a practical option for underwater localization. However, this telemetry network system needs to be pre-deployed and its availability cannot always be assumed. This study focuses on demonstrating the validity of terrain-based localization techniques in a GPS-denied underwater environment. Since terrain-based localization leads to a nonlinear estimation problem, nonlinear filtering methods are required to be employed. The extended Kalman filter (EKF) which is a widely used nonlinear filtering algorithm often shows limited performance under large initial uncertainty. The feasibility of using a particle filter is investigated, which can improve the performance and reliability of the terrain-based localization.

• Journal of Korea Water Resources Association
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• v.47 no.2
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• pp.119-133
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• 2014

The study adopted extended Kalman filter technique in an effort to predict Z-R relationship parameter as a stable value in real-time. Toward this end, a parameter estimation model was established based on extended Kalman filter in consideration of non-linearity of Z-R relationship. A state-space model was established based on a study that was conducted by Adamowski and Muir (1989). Two parameters of Z-R relationship were set as state variables of the state-space model. As a result, a stable model where a divergence of Kalman gain and state variables are not generated was established. It is noteworthy that overestimated or underestimated parameters based on a conventional method were filtered and removed. As application of inappropriate parameters might cause physically unrealistic rain rate estimation, it can be more effective in terms of quantitative precipitation estimation. As a result of estimation on radar rainfall based on parameters predicted with the extended Kalman filter, the mean field bias correction factor turned out to be around 1.0 indicating that there was a minor difference from the gauge rain rate without the mean field bias correction. In addition, it turned out that it was possible to conduct more accurate estimation on radar rainfall compared to the conventional method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.4
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• pp.826-832
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• 2007

A recurrent neural network has been frequently used in equalizing the channel for fast communication systems. The existing techniques, however, have mainly dealt with time-invariant chamois. The modern environments of communication systems such as mobile ones have the time-varying feature due to fading. In this paper, powerful decision feedback - recurrent neural network is used as channel equalizer for nonlinear and time-varying system, and two kinds of algorithms, such as extended Kalman filter (EKF) and sigma-point Kalman filter (SPKF), are proposed; EKF is for fast convergence and good tracing function, and SPKF for overcoming the problems which can be developed during the process of first linearization for nonlinear system EKF.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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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).

• Journal of Astronomy and Space Sciences
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• v.12 no.1
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• pp.143-156
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• 1995

Using a directional antenna, the Doppler effect of satellites can be detected and the orbital elements can be obtained by the Extended Kalman Filter with the observed frequency shift data. We obtained the orbital elements of NOAA-11 by the application of the Extended Kalman Filter type algorithm to the Doppler shift data of NOAA-11d and discussed the accuracy and the credibility of this algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.8
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• pp.780-788
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• 2006

This paper deals with INS/GPS tightly coupled integration algorithms using extend Kalman filter (EKF) and unscented Kalman filter (UKF). In the tightly coupled approach, nonlinear pseudorange measurement models are used for the INS/GPS integration Kalman filter. Usually, an EKF is applied for this task, but it may diverge due to poor functional linearization of the nonlinear measurement. The UKF approximates a distribution about the mean using a set of calculated sigma points and achieves an accurate approximation to at least second-order. We introduce the generalized scaled unscented transformation which modifies the sigma points themselves rather than the nonlinear transformation. The generalized scaled method is used to transform the pseudo range measurement of the tightly coupled approach. To compare the performance of the EKF- and UKF-based tightly coupled approach, real van test and simulation have been carried out with feedforward and feedback indirect Kalman filter forms. The results show that the UKF and EKF have an identical performance in case of the feedback filter form, but the superiority of the UKF is demonstrated in case of the feedforward filer form.

• Journal of Ocean Engineering and Technology
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• v.19 no.2 s.63
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• pp.74-81
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• 2005

A 3-D localization method of an autonomous underwater vehicle (AUV) has been developed, which can solve the limitations oj the conventional localization, such as LBL or SBL that reduces the flexibility and availability of the AUV. The system is composed of a mother ship (small unmanned marine prober) on the surface of the water and an unmanned underwater vehicle in the water. The mother ship is equipped with a digital compass and a GPS for position information, and an extended Kalman filter is used for position estimation. For the localization of the AUV, we used only non-inertial sensors, such as a digital compass, a pressure sensor, a clinometer, and ultrasonic sensors. From the orientation and velocity information, a priori position of the AUV is estimated by applying the dead reckoning method. Based on the extended Kalman filter algorithm, a posteriori position of the AUV is, then, updated by using the distance between the AUV and a mother ship on the surface of the water, together with the depth information from the pressure sensor.

• Journal of Engineering Education Research
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• v.19 no.6
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• pp.49-56
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• 2016

The Human Robot Interface (HRI) based on 3D depth sensor on the docent robot is developed and the localization algorithm based on extended Kalman Filter (EKFLA) are proposed through the capstone design by graduate students in this paper. In addition to this, the performance of the proposed EKFLA is also analyzed. The developed HRI system consists of the route generation and localization algorithm, the user behavior pattern awareness algorithm, the map data generation and building algorithm, the obstacle detection and avoidance algorithm on the robot control modules that control the entire behaviors of the robot. It is confirmed that the improvement ratio of the localization error in EKFLA on the scenarios 1-3 is increased compared with the localization algorithm based on Kalman Filter (KFLA) as 21.96%, 25.81% and 15.03%, respectively.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.6
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• pp.1413-1422
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• 2012

Adaptive back stepping control technique may provide robust control characteristics under parameter perturbation caused by changing external condition. In order to synthesize a high-precision velocity controller for IPMSM(Interior Permanent Magnet Synchronous Motor) using this method, the period of control loop should be very small. However, because of the resolution of the encoder for speed measurement, control cycle is limited, which makes it difficult to improve the performance of the controller. This paper proposes a velocity controller design method based on nonlinear adaptive back-stepping method to accomplish fast and accurate performance. Here, an EKF(Extended Kalman Filter) method is incorporated for the estimation of the motor speed into the design of a speed controller using adapted back-stepping control technique. The performance of the proposed controller is demonstrated through simulation using PSIM.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.9
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• pp.865-872
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• 2008

Spacecraft attitude estimation using the nonlinear unscented filter is addressed to fully utilize capabilities of the unscented transformation. To release significant computational load, an efficient technique is proposed by reasonably removing correlation between random variables. This modification introduces considerable reduction of sigma points and computational burden in matrix square-root calculation for most nonlinear systems. Unscented filter technique makes use of a set of sample points to predict mean and covariance. The general QUEST(QUaternion ESTimator) algorithm preserves explicitly the quaternion normalization, whereas extended Kalman filter(EKF) implicitly obeys the constraint. For spacecraft attitude estimation based on quaternion, an approach to computing quaternion means from sampled quaternions with guarantee of the quaternion norm constraint is introduced applying a constrained optimization technique. Finally, the performance of the new approach is demonstrated using a star tracker and rate-gyro measurements.