• 제어로봇시스템학회논문지
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• 제6권10호
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• pp.923-927
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• 2000

This paper presents a relationship between DOP for TOA and TDOA is defined using the error covariance matrix of TDOA. It is analytically shown that the error ellipsoid of TOA is as same as that of TDOA in magnitude and in orientation, which means that DOP for TOA is identical to the DOP for TDOA. By computer simulation, the positioning performance of two methods is compared, and we verify our assertion.

• Communications for Statistical Applications and Methods
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• 제16권4호
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• pp.675-686
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• 2009

In this paper, it will be assumed that there are two distinct populations which are multivariate normal with equal covariance matrix. We also assume that the two populations are equally likely and the costs of misclassification are equal. The classification rule depends on the situation when the training samples include missing values or not. We consider the bootstrap confidence intervals for classification error rate when a block of observation is missing.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1987년도 한국자동제어학술회의논문집; 한국과학기술대학, 충남; 16-17 Oct. 1987
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• pp.513-518
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• 1987

In this paper the error equations of the SDINS aligned by the gyrocompass are derived considering that the alignment errors are correlated to the sensor errors. Also the navigation errors due to the correlated errors are simulated by this error equations. The simulations are performed by the covariance analysis method, assumed all the sensor errors are random constants. The simulation results show that while the INS maintains the alignment attitude the cancellation takes place between the correlated errors, but once the INS changes attitude this cancellation effect is perturbed.

• 한국해양공학회지
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• 제31권4호
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• pp.315-323
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• 2017

This paper presents an integrated navigation system that combines ultra-short baseline (USBL), Doppler velocity log (DVL), and heading measurements for a deep-sea remotely operated vehicle, Hemire. A navigation model is introduced based on the kinematic relation of the position and velocity. The system states are predicted using the navigation model and corrected with the USBL, DVL, and heading measurements using the Kalman filter. The performance of the navigation system was confirmed through re-navigation simulations with the measured data at the Southern Mariana Arc submarine volcanoes. Based on the characteristics of the measurements, the design process for the parameters of the system modeling error covariance, measurement error covariance, and initial error covariance are presented. This paper reviews the influence of the outliers and blackout of the USBL and DVL measurements, and proposes an outlier rejection algorithm that is robust to USBL blackout. The effectiveness of the method is demonstrated with re-navigation for the data that includes USBL blackouts.

• 한국해양공학회지
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• 제17권6호
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• pp.83-90
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• 2003

This paper presents an underwater hybrid navigation system for a semi-autonomous underwater vehicle (SAUV). The navigation system consists of an inertial measurement unit (IMU), and a Doppler velocity log (DVL), accompanied by a magnetic compass. The errors of inertial measurement units increase with time, due to the bias errors of gyros and accelerometers. A navigational system model is derived, to include the scale effect and bias errors of the DVL, of which the state equation composed of the navigation states and sensor parameters is 20. The conventional extended Kalman filter was used to propagate the error covariance, update the measurement errors, and correct the state equation when the measurements are available. Simulation was performed with the 6-d.o,f equations of motion of SAUV, using a lawn-mowing survey mode. The hybrid underwater navigation system shows good tracking performance, by updating the error covariance and correcting the system's states with the measurement errors from a DVL, a magnetic compass, and a depth sensor. The error of the estimated position still slowly drifts in the horizontal plane, about 3.5m for 500 seconds, which could be eliminated with the help of additional USBL information.

• 제어로봇시스템학회논문지
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• 제13권12호
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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.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2016년도 추계학술대회 논문집
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• pp.59-60
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• 2016

Flooded lead acid batteries are still very popular in the industry because of their low cost as compared to their counterparts. State of Charge (SOC) estimation is of great importance for a flooded lead acid battery to ensure its safe working and to prevent it from over-charging or over-discharging. Different types of Kalman Filters are widely used for SOC estimation of batteries. The values of process and measurement noise covariance of a filter are usually calculated by trial and error method and taken as constant throughout the estimation process. While in practical cases, these values can vary as well depending upon the dynamics of the system. Therefore an Adaptive Unscented Kalman Filter (AUKF) is introduced in which the values of the process and measurement noise covariance are updated in each iteration based on the residual system error. A comparison of traditional and Adaptive Unscented Kalman Filter is presented in the paper. The results show that SOC estimation error by the proposed method is further reduced by 3 % as compared to traditional Unscented Kalman Filter.

• 한국산학기술학회논문지
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• 제14권5호
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• pp.2437-2442
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• 2013

Wi-Fi AP(Access Point)의 기반구조인 OFDM(Orthogonal Frequency Division Multiplexing)의 동일채널 간섭신호에 대한 취약성과 그 대책의 하나인 OFDM과 스마트안테나의 결합구조가 설명되었다. 높은 효율을 보장하지만 복잡성을 수반하는 FFT(Fast Fourier Transform) 후단 구조 대신 저렴한 구축비용이 장점인 수신신호 분산행렬 기반의 FFT 전단 스마트안테나의 수학적 모델이 전개되었다. 그 BER(Bit Error Rate) 특성을 높이기 위하여 제안된 채널행렬 출력 분산행렬을 기반으로 한 FFT 전단 구조 스마트안테나의 성능 측정을 위한 컴퓨터 모의실험이 수행되었다. 수신신호 분산행렬에 의해 생성된 가중치벡터에 비해 채널행렬 출력에 의한 가중치벡터가 다양한 페이딩 환경 변화에서 우월한 성능을 보임이 증명되었다.

• 한국항행학회논문지
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• 제21권1호
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• pp.138-144
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• 2017

본 논문에서는 INS/GPS결합 시스템에서 GPS가 기만신호 또는 지형지물에 의한 가시선이 제한되어 위성의 기하학적 배치가 저하되는 조건을 고려하였고, 통합항법 성능을 향상시키기 위한 방법을 제안하였다. 먼저 GPS의 DOP에 측정 공분산 이 연동되는 가변 공분산 확장 칼만필터(VCEKF)를 제시하였다. 그리고 몬테칼로 시뮬레이션을 통하여 EKF와 VCEKF를 사용한 통합항법 시스템의 항법성능을 분석하였다. DOP 값이 낮은 경우보다 DOP값이 높을 경우에 VCEKF가 확정 공분산을 사용하는 EKF보다 우수한 추정 성능을 보임을 확인할 수 있었다.

• International Journal of Aeronautical and Space Sciences
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• 제11권4호
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• pp.326-337
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• 2010

This work addresses problems regarding trajectory planning for unmanned aerial vehicle sensors. Such sensors are used for taking measurements of large nonlinear systems. The sensor investigations presented here entails methods for improving estimations and predictions of large nonlinear systems. Thoroughly understanding the global system state typically requires probabilistic state estimation. Thus, in order to meet this requirement, the goal is to find trajectories such that the measurements along each trajectory minimize the expected error of the predicted state of the system. The considerable nonlinearity of the dynamics governing these systems necessitates the use of computationally costly Monte-Carlo estimation techniques, which are needed to update the state distribution over time. This computational burden renders planning to be infeasible since the search process must calculate the covariance of the posterior state estimate for each candidate path. To resolve this challenge, this work proposes to replace the computationally intensive numerical prediction process with an approximate covariance dynamics model learned using a nonlinear time-series regression. The use of autoregressive time-series featuring a regularized least squares algorithm facilitates the learning of accurate and efficient parametric models. The learned covariance dynamics are demonstrated to outperform other approximation strategies, such as linearization and partial ensemble propagation, when used for trajectory optimization, in terms of accuracy and speed, with examples of simplified weather forecasting.