• 제어로봇시스템학회논문지
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• 제20권11호
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• pp.1175-1180
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• 2014

This paper proposes a new technique that produces improved local information using a low-cost GPS/INS system combined with Extended Kalman Filter and Path Planning when a Quad-rotor flies. In the research, a low-cost GPS is combined with INS by Extended Kalman Filter to improve local information. However, this system has disadvantages in that estimation accuracy is getting worsens when the Quad-rotor flies through the air in a curve and precision of location information is influenced by performance of the used GPS. An algorithm based on Path Planning is adopted to deal with these weaknesses. When the Quad-rotor flies outdoors, a short moving path can be predicted because all short moving paths of quad-rotor can be assumed to be straight. Path planning is used to make the short moving path and determine the closest local information of data of the GPS/INS system to location determined by path planning. Through the foregoing process, improved local data is obtained when the quad-rotor flies, and the performance of the proposed system is verified from various outdoor experiments.

• 한국군사과학기술학회지
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• 제14권3호
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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.

• Journal of Power Electronics
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• 제17권1호
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• pp.190-199
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• 2017

In this paper, a predictive direct power control (DPC) method based on a Kalman filter is presented for three-phase pulse-width modulation (PWM) rectifiers to improve the performance of rectifiers with source voltages that are distorted with harmonic components. This method can eliminate the most significant harmonic components of the source voltage using a Kalman filter algorithm. In the process of predicting the future real and reactive power to select an optimal voltage vector in the predictive DPC, the proposed method utilizes source voltages filtered by a Kalman filter, which can mitigate the adverse effects of distorted source voltages on control performance. As a result, the quality of the source currents synthesized using the PWM rectifier is improved, and the total harmonic distortion (THD) values are reduced, even under distorted source voltages.

• 전기학회논문지
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• 제60권12호
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• pp.2318-2325
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• 2011

The quality of float solution deeply influences the performance of CDGPS because the theories being used in the integer ambiguity resolution method are derived under the assumption of AWGN. But in real world, the properties of noises are far from AWGN, especially when multipath are concerned. It results in the bias in float solution which affects the success rate of integer ambiguity and the precision of position in CDGPS. This paper designs an augmented Kalman filter using shaping filter and Kalman filter for the performance improvement of CDGPS on multipath. The experimental results with real measurements show that the correct integer ambiguity is always found while the success rates of WLSQ and ordinary Kalman filter are 5% and 18%, respectively. Eventually, the position accuracy is also improved by using the proposed algorithm.

• Smart Structures and Systems
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• 제18권3호
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• pp.375-387
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• 2016

The classical Kalman filter (KF) can provide effective state estimation for structural identification and vibration control, but it is applicable only when external inputs are measured. So far, some studies of Kalman filter with unknown inputs (KF-UI) have been proposed. However, previous KF-UI approaches based solely on acceleration measurements are inherently unstable which leads to poor tracking and fictitious drifts in the identified structural displacements and unknown inputs in the presence of measurement noises. Moreover, it is necessary to have the measurements of acceleration responses at the locations where unknown inputs applied, i.e., with collocated acceleration measurements in these approaches. In this paper, it aims to extend the classical KF approach to circumvent the above limitations for general real time estimation of structural state and unknown inputs without using collocated acceleration measurements. Based on the scheme of the classical KF, an improved Kalman filter with unknown excitations (KF-UI) and without collocated acceleration measurements is derived. Then, data fusion of acceleration and displacement or strain measurements is used to prevent the drifts in the identified structural state and unknown inputs in real time. Such algorithm is not available in the literature. Some numerical examples are used to demonstrate the effectiveness of the proposed approach.

• 인터넷정보학회논문지
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• 제7권3호
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• pp.71-82
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• 2006

본 논문은 실내 환경에서 이동객체의 실시간 추적 알고리즘을 제안한다. 제안하는 시스템은 기존에 사용되던 삼각측량 기법과 DCM(Database Correlation Method) 기법을 통해 각각의 위치 데이터를 생성한 후, 그 중 이동객체와 더 근사한 위치 데이터를 실시간으로 선택한다. 또한 Kalman Filter를 사용하여 선택된 위치 데이터를 보정하여 시스템에 적용하므로 이동 객체의 위치 정확도를 향상시켰다. 기존에 연구된 Kalman Filter는 과거의 정보를 이용하여 현재의 위치를 추정해 내는 시스템의 특성상 안정화 되는 시간까지 불확실한 위치 데이터를 가지게 된다. 하지만 제안하는 위치 추적 시스템은 기존의 Kalman Filter를 그대로 적용하지 않고, 더 효율적인 방안을 제시한 후 적용함으로 더 정확한 위치 추적을 가능케 한다.

• 대한음성학회지:말소리
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• 제57호
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• pp.85-97
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• 2006

This paper suggests an algorithm that can estimate the direction of the sound source in real time. The algorithm uses the time difference and sound intensity information among the recorded sound source by four microphones. Also, to deal with noise of robot itself, the Kalman filter is implemented. The proposed method can take shorter execution time than that of an existing algorithm to fit the real-time service robot. Also, using the Kalman filter, signal ratio relative to background noise, SNR, is approximately improved to 8 dB. And the estimation result of azimuth shows relatively small error within the range of ${\pm}7$ degree.

• 한국자동차공학회논문집
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• 제10권3호
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• pp.123-129
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• 2002

This paper proposes a vehicle trajectory prediction method far application to vehicle-to-vehicle distance control. This method is based on 2-dimensional kinematics and a Kalman filter has been used to estimate acceleration of the object vehicle. The simulation results using the proposed control method show that the relative distance characteristics can be improved via the trajectory prediction method compared to the customary intelligent cruise control algorithm.

• 전자공학회논문지
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• 제52권5호
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• pp.190-196
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• 2015

본 논문은 항법장치 위치보정을 위해 칼만필터를 적용한 GPS/INS융합의 다중보정방법을 제안한다. 연구에서는 관성항법장치를 구현하기 위해 9축 항법장치로 보정알고리즘을 적용하여 위치오차를 감소시킨 방법을 적용했다. 일반적으로 GPS/INS는 위치정보를 얻어낼 수 있지만 위치정보를 구하는 과정에서 오차 또한 더불어 커지게 되기에 이를 보정하기 위한 강인한 오차 보정 알고리즘이 필요하다. 본 논문에서는 9축 관성센서(mpu-9150)의 외란에 대한 강인성 향상을 위해 가속도계 보정 알고리즘을 사용하여 tilt보정을 수행했으며, 제어 대상체의 정확한 방위를 파악할 수 있도록 Yaw각 재정의 알고리즘을 적용하였다. 최종적으로 GPS/INS와 칼만 필터를 함께 결합한 통합시스템을 구현하였다.

• 한국측량학회지
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• 제37권5호
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• pp.367-377
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• 2019

As an alternative navigation system for the non-GNSS (Global Navigation Satellite System) environment, a new type of DBRN (DataBase Referenced Navigation) which applies both gravity gradient and terrain, and combines filter-based algorithm with profile matching was suggested. To improve the stability of the performance compared to the previous study, both centralized and decentralized EKF (Extended Kalman Filter) were constructed based on gravity gradient and terrain data, and one of filters was selected in a timely manner. Then, the final position of a moving vehicle was determined by combining a position from the filter with the one from a profile matching. In the simulation test, it was found that the overall performance was improved to the 19.957m by combining centralized and decentralized EKF compared to the centralized EKF that of 20.779m. Especially, the divergence of centralized EKF in two trajectories located in the plain area disappeared. In addition, the average horizontal error decreased to the 16.704m by re-determining the final position using both filter-based and profile matching solutions. Of course, not all trajectories generated improved performance but there is not a large difference in terms of their horizontal errors. Among nine trajectories, eights show smaller than 20m and only one has 21.654m error. Thus, it would be concluded that the endemic problem of performance inconsistency in the single geophysical DB or algorithm-based DBRN was resolved because the combination of geophysical data and algorithms determined the position with a consistent level of error.