On the optimality of two-stage state estimation in the presence of random bias

Alouani, A.T.; Xia, Peng; Rice, Theodore R.; Blair, W.D.

doi:10.1109/9.233168

Cited by 88 publications

(48 citation statements)

References 5 publications

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“…In the numerical simulation of solute transport in groundwater, there are unavoidable bias: the error of the model itself, the error in the field measurement, and the error in the process of the solution [7]. In order to get closer to the real parameters, identification results and water quality prediction results, it is necessary to minimize the influence of these bias [8].…”

Section: Introductionmentioning

confidence: 99%

Two-Stage Cubature Kalman Filterand Its Application in Water Pollution Model

Zhang¹,

Xu²,

Hai-lu³

2018

Acta sci. Malays.

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Water Pollution Model is a nonlinear system which present the random bias. The most common method is to use augmented state Cubature Kalman Filter, but the computational requirement of augmented state Kalman filter may become excessive. It is easily overflow and fail when running on digital computer. In this paper, two-stage Cubature Kalman filter is proposed to solve this problem. The estimate of two-stage Cubature Kalman filter can be expressed as the output of the advanced bias free filter and bias filter. Contrast augmented state Cubature Kalman filter, twostage Cubature Kalman filter is equivalent to the augmented state Cubature Kalman Filter in terms of computational accuracy, but computation is much smaller than augmented state Cubature Kalman Filter. The simulation results prove the validity of the two-stage Cubature Kalman filter in Water Pollution Model and prove the equivalence of the two algorithms.

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Section: Introductionmentioning

confidence: 99%

Two-Stage Cubature Kalman Filterand Its Application in Water Pollution Model

Zhang¹,

Xu²,

Hai-lu³

2018

Acta sci. Malays.

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“…and the a priori and a posteriori covariance matrices, P À x ðkÞ and P x ðkÞ, of the adjusted value of x are given by [4][5][6] P…”

Section: Optimal Separate-bias Kalman Filtermentioning

confidence: 99%

“…4) To account for the bias walk, the process noise covariance was increased heuristically, and optimality conditions were derived. 5,6) On the other hand, Zanetti and Bishop purposed algorithms for precise navigation are derived to include uncompensated bias terms in both the process and measurement noise. 7) In their work, the effects of the noise and biases were considered as sources of uncertainty and not as elements of the state vector.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Tuning Separate-Bias Extended Kalman Filter for Attitude Estimation

Matunaga

2014

Trans. Japan Soc. Aero. S Sci.

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This paper provides a new method for robust spacecraft attitude estimation in the presence of measurement biases. The proposed method is developed based on the separate-bias or two-state Kalman filter which was first introduced by Friedland. The separate-bias Kalman filter consists of two stages: the first stage, the ''bias-free'' filter, is based on the assumption that the bias is nonexistent; the second stage, the ''bias'' filter, is implemented to estimate bias vectors. The output of the first filter is then corrected with the output of the second filter. In this research, the authors propose a real-time tuning method for a parameter in the Kalman gain calculation process of the ''bias'' filter. The adaptive scale factor is optimized relying on the minimization of the cost function, which is calculated from the difference between the predicted and measurement values. The proposed filter has a faster convergence speed from large initial errors and an increased accuracy on unpredicted bias models than conventional methods. Moreover, to verify these advantages, the research also provides analyses and comparisons between the proposed method with conventional methods like the original separate-bias Kalman filter, unscented Kalman filter and extended Kalman filter in several numerical simulation scenarios for a microsatellite.

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“…이러한 바이어스는 표 †2011년 9월 2일 접수～2011년 11월 25일 게재승인 * 한양대학교(Hanyang University) ** 인하대학교(Inha University) 책임저자 : 김형원(khwhy8858@naver. [8] 이 연구되었다. 최근에는 다수 센서에서 획 득한 동일 표적의 측정치들을 이용하여 표적의 상태 변수를 제거한 state independent pseudo measurement를 생성하고 이를 통해 바이어스를 추정하는 EX기법 [9] 이 연구되었는데 최적화된 성능을 가진다고 알려져 있다.…”

Section: 센서의 측정치 획득과정에서 생성되는 바이어스는unclassified

Multisensor Bias Estimation with Pseudo Measurement for Asynchronous Sensors

Kim¹,

Kim²,

Park³

et al. 2011

Journal of the Korea Institute of Military Science and Technolo

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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. [7] 가 있으나 바이어스 항의 dimension증가에 따라 계산량이

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On the optimality of two-stage state estimation in the presence of random bias

Cited by 88 publications

References 5 publications

Two-Stage Cubature Kalman Filterand Its Application in Water Pollution Model

Two-Stage Cubature Kalman Filterand Its Application in Water Pollution Model

Real-Time Tuning Separate-Bias Extended Kalman Filter for Attitude Estimation

Multisensor Bias Estimation with Pseudo Measurement for Asynchronous Sensors

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