The effect of missing data on the steady-state performance of an alpha , beta tracking filter

Kassel, Raymond John; Baxa, E. G.

doi:10.1109/ssst.1988.17106

Cited by 3 publications

(3 citation statements)

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“…However, the use of Kalman filters requires prior knowledge about the system under consideration and if these are not appropriately known, the estimation process will not be optimal and it may even diverge. In addition , it is assumed that all the observations are available during estimation, since Kalman filters are sensitive to incomplete or missing measurements [12], [13], [15]. In real-world applications, these assumptions are not always accurate; since there are applications for which the exact system model is hard to obtain and only approximations to the real model are available.…”

Section: Introductionmentioning

confidence: 99%

Robust filtering for uncertain discrete-time systems with uncertain noise covariance and uncertain observations

Mohamed

Nahavandi

2008

2008 6th IEEE International Conference on Industrial Informatics

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The use of Kalman filtering is very common in state estimation problems. The problem with Kalman filters is that they require full prior knowledge about the system modeling. It is also assumed that all the observations are fully received. In real applications, the previous assumptions are not true all the time. It is hard to obtain the exact system model and the observations may be lost due to communication problems. In this paper, we consider the design of a robust Kalman filter for systems subject to uncertainties in the state and white noise covariances. The systems under consideration suffer from random interruptions in the measurements process. An upper bound for the estimation error covariance is proposed. The proposed upper bound is further minimized by selection of optimal filter parameters. Simulation example shows the effectiveness of the proposed filter.

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Section: Introductionmentioning

confidence: 99%

Robust filtering for uncertain discrete-time systems with uncertain noise covariance and uncertain observations

Mohamed

Nahavandi

2008

2008 6th IEEE International Conference on Industrial Informatics

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“…The problem was studied in the context of signal estimation but not in the context of multisensor data fusion [2], [3].…”

Section: Introductionmentioning

confidence: 99%

Optimal multisensor data fusion for linear systems with missing measurements

Mohamed

Nahavandi

2008

2008 IEEE International Conference on System of Systems Engineering

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Multisensor Data fusion has attracted a lot of research in recent years. It has been widely used in many applications especially military applications for target tracking and identification. In this paper, we will handle the multisensor data fusion problem for systems suffering from the possibility of missing measurements. We present the optimal recursive fusion filter for measurements obtained from two sensors subject to random intermittent measurements. The noise covariance in the observation process is allowed to be singular which requires the use of generalized inverse. Dlustration example shows the effectiveness of the proposed filter in the measurements loss case compared to the available optimal linear fusion methods.

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“…As for filtering problem, only a very limited number of filter design methods for system output signals with missing measurements have been developed. In [11], the effect of missing data on the steady-state performance of a tracking filter was shown to be crucial. In [6], Chen proposed a suboptimal Kalman filtering method to cope with the case of measurement data missing.…”

Section: Introductionmentioning

confidence: 99%

Variance-constrained filtering for uncertain stochastic systems with missing measurements

Wang

Liu

2003

IEEE Trans. Automat. Contr.

306

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In this note, we consider a new filtering problem for linear uncertain discrete-time stochastic systems with missing measurements. The parameter uncertainties are allowed to be norm-bounded and enter into the state matrix. The system measurements may be unavailable (i.e., missing data) at any sample time, and the probability of the occurrence of missing data is assumed to be known. The purpose of this problem is to design a linear filter such that, for all admissible parameter uncertainties and all possible incomplete observations, the error state of the filtering process is mean square bounded, and the steady-state variance of the estimation error of each state is not more than the individual prescribed upper bound. It is shown that, the addressed filtering problem can effectively be solved in terms of the solutions of a couple of algebraic Riccati-like inequalities or linear matrix inequalities. The explicit expression of the desired robust filters is parameterized, and an illustrative numerical example is provided to demonstrate the usefulness and flexibility of the proposed design approach.

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The effect of missing data on the steady-state performance of an alpha , beta tracking filter

Cited by 3 publications

References 0 publications

Robust filtering for uncertain discrete-time systems with uncertain noise covariance and uncertain observations

Robust filtering for uncertain discrete-time systems with uncertain noise covariance and uncertain observations

Optimal multisensor data fusion for linear systems with missing measurements

Variance-constrained filtering for uncertain stochastic systems with missing measurements

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