• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.352-355
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• 1995

In this paper, estimation error bounds of the optimal FIR (Finite Impulse Response) filter, which is proposed by Kwon et al.[1, 2], are presented in discrete-time systems with the model uncertainty. Performance bounds are here represented by the upper bounds on the difference of the estimation error covariances between the nominal and real values in case of the systems with the noise or model parameter uncertainty. The estimation error bounds of the discrete-time optimal FIR filter is compared with those of the Kalman filter via a numerical example applied to the simulation problem by Toda and Patel[3]. Simulation results show that the former has robuster performance than the latter.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.11
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• pp.48-56
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• 2005

Predictive filter theory proposed recently can be characterized by inherent advantages of estimating modelling error and overcoming the disadvantage of the Kalman filter theory. A one-step ahead error is minimized to produce optimized filter performance in the form of the predictive filter. The main advantage of this filter lies in the ability to estimate both state vector and system model error. In this paper, attitude estimation results based upon the predictive filter theory is addressed. Mathematical formulation for estimating bias signal is peformed by using the predictive filter theory, and attitude estimation based upon vector observation is presented. From the results of this study, the potential applicability of the predictive filter is highlighted.

• Journal of Advanced Navigation Technology
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• v.8 no.1
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• pp.26-37
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• 2004

Consistent and realistic error covariance information is important for position estimation, error analysis, fault detection, and integer ambiguity resolution for differential GNSS. In designing a position domain carrier-smoothed-code filter where incremental carrier phases are used for time-propagation, formulation of consistent error covariance information is not easy due to being bounded and temporal correlation of propagation noises. To provide consistent and correct error covariance information, this paper proposes two recursive filter algorithms based on carrier-smoothed-code techniques: (a) the stepwise optimal position projection filter and (b) the stepwise unbiased position projection filter. A Monte-Carlo simulation result shows that the proposed filter algorithms actually generate consistent error covariance information and the neglection of carrier phase noise induces optimistic error covariance information. It is also shown that the stepwise unbiased position projection filter is attractive since its performance is good and its computational burden is moderate.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.864-866
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• 1996

The attitude error is expressed using four kinds of quaternion errors. And the explicit relation equations between them are derived four kinds of nonlinear error models of SDINS using the their explicit relation are also proposed for a nonlinear filter which may be available for a system in the presence of a large attitude error the concept of the proposed nonlinear error model is applied to the velocity aided SDINS using a linear Kalman filter and an extended Kalman filter the simulation results reveal a improvement of performance using the nonlinear error model.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.12
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• pp.1230-1242
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• 2007

This paper describes an IIR/FIR fusion filter for a nonlinear system, and analyzes the stability of the fusion filter. The fusion filter is applied to INS/GPS integrated system, and the performance is verified by simulation and experiment. In the fusion filter, an IIR-type filter (SPKF) and FIR-type filter (MRHKF filter) are processed independently, then the two filters are merged using the mixing probability calculated using the residuals and residual covariance information of the two filters. The merits of the SPKF and the MRHKF filter are embossed and the demerits of the filters are diminished via the filter fusion. Consequently, the proposed fusion filter has robustness against to model uncertainty, temporary disturbing noise, large initial estimation error, etc. The stability of the fusion filter is verified by showing the closeness of the states of the two sub filters in the mixing/redistribution process and the upper bound of the error covariance matrices. This fusion filter is applied into INS/GPS integrated system, and important factors for filter processing are presented. The performance of the INS/GPS integrated system designed using the fusion filter is verified by simulation under various error environments and is confirmed by experiment.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.3
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• pp.517-523
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• 2011

The estimation filter in radar systems must track targets' position within low tracking error. In the Multi-Function Radar(MFR), ${\alpha}-{\beta}$ filter and Kalman filter are widely used to track single or multiple targets. However, due to target maneuvering, these filters may not reduce tracking error, therefore, may lost target tracks. In this paper, a target tracking filter based on particle filtering algorithm is proposed for the MFR. The advantage of this method is that it can track targets within low tracking error while targets maneuver and reduce impoverishment of particles by the proposed resampling method. From the simulation results, the improved tracking performance is obtained by the proposed filtering algorithm.

• International Journal of Aeronautical and Space Sciences
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• v.18 no.1
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• pp.118-128
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• 2017

In this study, we propose a real time inertial navigation system/global positioning system (INS/GPS) integrated smoothing algorithm based on an extended Kalman filter (EKF) and a position damping loop (PDL) for synthetic aperture radar (SAR). Integrated navigation algorithms usually induce discontinuities due to error correction update by the Kalman filter, which are as detrimental to the performance of SAR as the relative position error. The proposed smoothing algorithm suppresses these discontinuities and also reduces the relative position error in real time. An EKF estimates the navigation errors and sensor biases, and all the errors except for the position error are corrected directly and instantly. A PDL activated during SAR operation period imposes damping effects on the position error estimates, where the estimated position error is corrected smoothly and gradually, which contributes to the real time smoothing and small relative position errors. The residual errors are re-estimated by the EKF to maintain the estimation performance and the stability of the overall loop. The performance improvements were confirmed by Monte Carlo simulations. The simulation results showed that the discontinuities were reduced by 99.8% and the relative position error by 48% compared with a conventional EKF without a smoothing loop, thereby satisfying the basic performance requirements for SAR operation. The proposed algorithm may be applicable to low cost SAR systems which use a conventional INS/GPS without changing their hardware configurations.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2016.05a
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• pp.302-304
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• 2016

The purpose of this paper is to draw comparison between the performance of ${\alpha}-{\beta}-{\gamma}$ filter and Kalman filter of a tracking module for ARPA system on board high dynamic warship. The comparison is based on the filters' capability to reduce residual error and maintain a stable transient response. The residual error is computed from the difference between the observed the predicted positions for the entire tracking period. The results indicate that the Kalman filter has a higher tracking accuracy compared to the optimal ${\alpha}-{\beta}-{\gamma}$ filter. However, both filters have a similar transient response.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.236-239
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• 1999

This paper describes a low-power design of decimation filter. To reduce power consumption, an approximate processing method which controls the error in canonic signed digit(CSD) coefficients is proposed. The CSD representation reduces the number of operations by representing multiplications with add and shift operations. The proposed method further reduces the number of operations by controlling the error of CSD coefficient. Processor type architecture is used to implement the proposed method. Simulation result shows that the number of operations is reduced to 56%, 35% and 10% at each approximated filter level.

• Journal of Biomedical Engineering Research
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• v.10 no.2
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• pp.83-88
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• 1989

This paper is a study on the design of adptive filter for QRS complex detection. We propose a simple adaptive algorithm to increase capability of noise cancelation in QRS complex detection with two stage adaptive filter. At the first stage, background noise is removed and at the next stage, only spectrum of QRS complex components is passed. Two adaptive filters can afford to keep track of the changes of both noise and QRS complex. Each adaptive filter consists of prediction error filter and FIR filter The impulse response of FIR filter uses coefficients of prediction error filter. The detection rates for 105 and 108 of MIT/BIH data base were 99.3% and 97.4% respectively.