• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.46 no.3
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• pp.219-229
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• 2018

This paper proposes Kalman Filter-based relative navigation algorithms for proximity tasks such as rendezvous/docking/cluster-operation of spacecraft using PSD Sensors and Infrared Beacon Modules. Numerical simulations are performed for comparative analysis of the performance of each relative-navigation technique. Based on the operation principle and optical modeling of the PSD Sensor and the Infrared Beacon Module used in the relative navigation algorithm, a measurement model for the Kalman filter is constructed. The Extended Kalman Filter(EKF) and the Unscented Kalman Filter(UKF) are used as probabilistic relative navigation based on measurement fusion to utilize kinematics and dynamics information on translational and rotation motions of satellites. Relative position and relative attitude estimation performance of two filters is compared. Especially, through the simulation of various scenarios, performance changes are also investigated depending on the number of PSD Sensors and IR Beacons in target and chaser satellites.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.4
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• pp.781-790
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• 2008

In order to improve on the divergence by noise convariance in the Kalman filter position estimation, we propose a method of position estimating through compensating the autonomous mobile robot's noise. Proposed method is the modified Kalman filter using neural network. It is prevented the divergence by the estimation of measurement noise covariance and system noise covariance. In order to verify the effectiveness of the proposed method, we performed simulations and experiments for position estimation. The results show that convergence and position error is reduced than the Kalman filter method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.7
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• pp.1332-1337
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• 2006

In this paper, a noble second stage hetero-estimator is used for positioning error detection in mobile robot. Previous methods are either expensive in the case of positioning error correction or not able to detect positioning error. To overcome the latter shortage, the positioning error detection is performed using second stage hetero-estimator in motor model of mobile robot without any additional costs. A Kalman filter in the estimator gets the residual of motor current and an adaptive self-tunning filter checks the whiteness of the residual. Some simulation results show the possibility of the proposed method.

• Journal of Astronomy and Space Sciences
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• v.22 no.2
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• pp.175-186
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• 2005

In this paper, the development of the extended kalman filter(EKF) which is based on Koreasat-3 bus system is introduced and the design result is shown through the simulation. Especially to determine the filter gains for accurate estimation, there is assumed that initial estimated parameters are not changed. But although the satellite performs the attitude control by 2Hz, it is verified that the EKF is running rightly using the changed filter gains. Also some cases are considered using the simulation : with each bias for 4-axis gyro and with gyro each axis failure. It is verified that the designed filter can be used as the back-up about gyro failure.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.7
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• pp.59-69
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• 2005

A position domain Hatch filter is proposed as an efficient carrier-smoothed-code processing algorithm for real-time kinematic differential global satellite navigation systems. The well-known range domain Hatch filter is newly interpreted with a stochastical point of view. The interpretation result is extended to derive the position domain Hatch filter. By a covariance simulation, it is shown that Hatch gain is, in general, more efficient than Kalman-type gain in carrier-smoothed-code processing and the proposed position domain Hatch filter is more advantageous than the conventional range domain Hatch filter if the visible satellite constellation changes during the positioning task.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.2
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• pp.53-58
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• 2021

In this paper, an alternative finite memory structure(FMS) smoothing filter is developed for discrete-time state-space model with a control input. To obtain the FMS smoothing filter, unbiasedness will be required beforehand in addition to a performance criteria of minimum variance. The FMS smoothing filter is obtained by directly solving an optimization problem with the unbiasedness constraint using only finite measurements and inputs on the most recent window. The proposed FMS smoothing filter is shown to have intrinsic good properties such as deadbeat and time-invariance. In addition, the proposed FMS smoothing filter is shown to be equivalent to existing FMS filters according to the delay length between the measurement and the availability of its estimate. Finally, to verify intrinsic robustness of the proposed FMS smoothing filter, computer simulations are performed for a temporary model uncertainty. Simulation results show that the proposed FMS smoothing filter can be better than the standard FMS filter and Kalman filter.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.8
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• pp.148-155
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• 2014

In this research a signal processing system is designed for detecting the ultrasonic signal envelope using Xilinx's Zynq SoC(system on chip). As a design tool, Vivado IDE(integrated design environment) is used to hierarchically design the whole signal processing system. The proposed system consists of a Zynq-internal ADC, an FIR(finite impulse response) BPF(band pass filter), an absolute value calculator, an FIR LPF(lpw pass filter), and the Kalman filter. Under this configuration, two design schemes, HW design scheme with LPF as a final stage and HW/SW co-design scheme with a Kalman filter as a final stage, are compared in terms of the performance and efficiency. As a result, envelope detecting performances of the two schemes are proved to be almost same, but the HW/SW co-design is verified to be much more efficient than the HW design considering the much smaller time consumption during system design.

• Journal of Broadcast Engineering
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• v.20 no.4
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• pp.568-579
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• 2015

Recently, mobile devices with touch display panel are widely used. Accuracy and reaction speed of touch signal are very important in touch devices. Therefore, we need to develop an effective algorithm to reduce touch noise quickly and accurately. This paper proposes a touch noise reduction algorithm using Kalman filtering in consideration of signal motion. First, a specific pre-emphasis processing is applied to an input signal so as to maximize the effect of Kalman filtering. In other words, a pure signal in the touch signal increases but noise in the touch signal decreases. Next, motion of the signal is detected. Motion estimation is performed only if motion is detected. If we detect motion by using the only neighborhood of the signal, we can reduce about 75% of the computation in comparison with examining the entire area. Finally, Kalman filtering using the previous state of current signal is performed. Experimental results show that the proposed algorithm suppresses touch noise sufficiently without degradation of the pure signal

• Journal of Korea Water Resources Association
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• v.45 no.7
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• pp.697-711
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• 2012

This study applied the extended Kalman Filter, a data assimilation method, for the real-time quality improvement of runoff measurements. The state-space model of the extended Kalman Filter was composed of a rainfall-runoff model and the runoff measurement. This study divided the purpose of quality improvement of runoff measurements into two; one is to suppress the abnormally high variation of dam inflow data, and the other to amend the missing or erroneous measurements. For each case, a proper model of extended Kalman Filter was proposed, and the main difference between two models is whether only the variation is considered or both the bias and variation are considered in the estimation of covariance function. This study was applied to the Chungju Dam Basin to confirm the proposed models were effectively worked to improve the quality of both the dam inflow data and the runoff measurements with some missing and erroneous part.