• 한국군사과학기술학회지
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• 제14권6호
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• pp.1198-1206
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• 2011

In this paper, a sensor bias estimation method with pseudo measurement for asynchronous multisensor systems is proposed. The proposed bias estimation method separates the local filter which estimates the target state with biased measurements into two parts, one is bias part, the other is target state part. By using these two parts, the algorithm generates the pseudo bias measurement for estimating bias, and then eliminates bias of local track through bias compensation. Finally, the proposed algorithm is evaluated by comparing with the existing EXX method.

• 로봇학회논문지
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• 제4권2호
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• pp.112-120
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• 2009

In this paper, a localization error recoverymethod based on bias estimation is provided for outdoor localization of mobile robot using different-type sensors. In the previous data integration method with DGPS, it is difficult to localize mobile robot due to multi-path phenomena of DGPS. In this paper, fault data due to multi-path phenomena can be recovered by bias estimation. The proposed data integration method uses a Kalman filter based estimator taking into account a bias estimator and a free-bias estimator. A performance evaluation is shown through an outdoor experiment using mobile robot.

• 한국군사과학기술학회지
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• 제15권5호
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• pp.676-686
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• 2012

This paper presents a sensor bias estimation method with serial fusion for asynchronous multisensory systems. Serial fusion processes the sensor measurements in a first-come-first-serve basis and it plays an essential role in asynchronous fusion in practice. The proposed algorithm generates the bias measurements using fusion estimates and sensor measurements for bias estimation, and compensates the sensor biases in fusion tracks. A simulation study indicates that the proposed algorithm has the superior performance in bias estimation and accurate tracking.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권9호
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• pp.2464-2482
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• 2024

The semantic understanding of numbers requires association with context. However, powerful neural networks overfit spurious correlations between context and numbers in training corpus can lead to the occurrence of contextual bias, which may affect the network's accurate estimation of number magnitude when making inferences in real-world data. To investigate the resilience of current methodologies against contextual bias, we introduce a novel out-of-distribution (OOD) numerical question-answering (QA) dataset that features specific correlations between context and numbers in the training data, which are not present in the OOD test data. We evaluate the robustness of different numerical encoding and decoding methods when confronted with contextual bias on this dataset. Our findings indicate that encoding methods incorporating more detailed digit information exhibit greater resilience against contextual bias. Inspired by this finding, we propose a digit-aware position embedding strategy, and the experimental results demonstrate that this strategy is highly effective in improving the robustness of neural networks against contextual bias.

• 한국우주과학회:학술대회논문집(한국우주과학회보)
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• 한국우주과학회 2010년도 한국우주과학회보 제19권1호
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• pp.27.5-28
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• 2010

The unexpected observation condition or insufficient measurement modeling can lead to uncertain measurement errors. The uncertain measurement error of orbit determination problem typically consists of noise, bias and drift. It must be removed by using a proper estimation process for better orbit accuracy. The estimation of noise and drift is not easy because of their random or unpredictable variation. On the other hand, bias is a constant difference between the mean of the measured values and the true value, so it can be simply removed. In this study, precision orbit determination with SLR observations considering range bias estimation is presented. The Yonsei Laser-ranging Precision Orbit Determination System (YLPODS) and SLR NP (Normal Point) observations of CHAMP satellite are used for this work. The SLR residual test is performed to estimate the range bias of each arc. The result shows that we can get better orbit accuracy through range bias estimation.

• 한국항공우주학회지
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• 제36권11호
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• pp.1126-1131
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• 2008

본 논문은 궤도 기하학 기반 바이어스 추정기법을 다목적실용위성 1호 및 2호의 자기센서 측정데이터에 적용하여 위성체 태양전지판과 전장박스에서 발생하는 유도자기장 바이어스를 추정한다. 유도자기장 바이어스의 추정과 적절한 보정은 자기센서의 노후화를 대처하고 수명을 최대한 연장하여 정상적으로 위성 임무를 수행을 가능하게 한다.

• 한국항공우주학회지
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• 제31권1호
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• pp.58-66
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• 2003

본 논문에서는 무궁화위성을 대상으로 추적 안테나의 바이어스를 추정하기 위한 방안들을 제시하고 있다. 먼저 거리, 방위각, 앙각에 이어 선회시선거리를 포함하는 배치필터를 구성하였으며 시뮬레이션을 통하여 바이어스 추정성능 변화를 분석하였다. 또한 결과를 보완하기 위하여 정밀하게 보정된 타 추적 안테나의 정보를 이용하여 대상 안테나의 바이어스를 정확하게 예측하기 위한 방안을 제시하였다. 마지막으로 안테나 바이어스 추정 결과를 분석하고 평가할 수 있도록 안테나 바이어스간의 상관관계에 대한 분석을 수행하였다.

• 제어로봇시스템학회논문지
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• 제17권8호
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• pp.783-789
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• 2011

Target tracking performance improvement using multi-sensor data fusion is a challenging work. However, biases in the measurements should be removed before various data fusion techniques are applied. In this paper, a bias removing algorithm using measurement data from multi-radar tracking systems is proposed and evaluated by computer simulation. To predict bias estimation performance in various geometric relations between the radar systems and target, a system observability index is proposed and tested via computer simulation results. It is also studied that target tracking which utilizes multi-sensor data fusion with bias-removed measurements results in better performance.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.32-35
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• 1996

In this paper, it is shown that the dominant errors of baro-altimeters can be characterized by bias and scale factor errors. Also an optimal filter for estimating both bias and scale factor is derived based on the concept of model transition. The optimal filter is, however, not realizable because the model transition hypotheses increase exponentially. Therefore a realizable suboptimal filter using the interacting multiple model(IMM) technique is proposed. Computer simulation results show that the estimation errors of the proposed filter are smaller than those of the conventional least squares algorithm with a forgetting factor when both the bias and the scale factor are varying.

• Communications for Statistical Applications and Methods
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• 제5권2호
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• pp.339-351
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• 1998

Designs for discriminating between two linear regression models are studied under $\Lambda$-type optimalities maximizing the measure for the lack of fit for the designs with fixed model inadequacy. The problem of selecting an appropriate $\Lambda$-type optimalities is shown to be closely related to the estimation method. $\Lambda$-type optimalities for the least squares and minimum bias estimation methods are considered. The minimum bias designs are suggested for the designs invariant with respect to the two estimation methods. First order minimum bias designs optimal under $\Lambda$-type optimalities are then derived. Finally for the case where the lack of fit test is significant, an approach to the construction of a second order design accommodating the optimal first order minimum bias design is illustrated.