Hierarchical adaptive Kalman filtering for interplanetary orbit determination

Chaer, W.S.; Ghosh, Joydeep

doi:10.1109/7.705895

Cited by 14 publications

(20 citation statements)

References 18 publications

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“…An orbit tracking filter similar to this problem is found in Chaer et al (1998). Positioning in GSM cellular phone systems using range information of the same kind as in the GPS is reported in Pent et al (1997).…”

Section: Linearized Kalman Filter Approachmentioning

confidence: 92%

Adaptive Filtering and Change Detection

Gustafsson¹

2001

602

699

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Section: Linearized Kalman Filter Approachmentioning

confidence: 92%

Adaptive Filtering and Change Detection

Gustafsson¹

2001

602

699

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“…When one employs a hierarchy of experts, the above problem is circumvented as hierarchy provides isolation of subspaces [19]. Unfortunately, the computational cost increases as the tree height increases, as more gating and expert networks need to be trained.…”

Section: A Generic Mixture Of Experts Architecturementioning

confidence: 99%

Structurally adaptive modular networks for nonstationary environments

Ramamurti

Ghosh

1999

IEEE Trans. Neural Netw.

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This paper introduces a neural network capable of dynamically adapting its architecture to realize time variant nonlinear input-output maps. This network has its roots in the mixture of experts framework but uses a localized model for the gating network. Modules or experts are grown or pruned depending on the complexity of the modeling problem. The structural adaptation procedure addresses the model selection problem and typically leads to much better parameter estimation. Batch mode learning equations are extended to obtain on-line update rules enabling the network to model time varying environments. Simulation results are presented throughout the paper to support the proposed techniques.

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“…A main weakness of the FR approach arises from the lack of solid, systematic weight update, while the PS approach has a built-in solid mechanism for sequential update of the weights thanks to Bayes' rule. The FRS relies on heuristic principles and expert systems for weight update, while the "mixture of experts" of [24,25] had recourse to an optimization searching tree algorithm with the help of Bayes' rule.…”

Section: Definition 23 ([11]mentioning

confidence: 99%

“…[9,11,14,17,20,[22][23][24][25][26]29] for more detailed information), according to prior knowledge, the fuzzy rough theory and the probability theory are used in different levels in applicationthat is, when all probabilities of conditional items and statistical distributions are known, the probability theory is usually used; however, the fuzzy theory, the rough sets theory and the fuzzy rough theory are separately utilized when some probabilities of conditional items and statistical distributions are unknown. In addition, the fuzzy rough theory and the probability theory can be used simultaneously when the prior knowledge satisfies the requirement of probability.…”

Section: Step Of Ps Algorithmmentioning

confidence: 99%

“…In the past, the probability statistics (PS) method [11,[22][23][24][25] was usually used for target control. Along with the development of information and industry technology, at present, people are becoming more and more interested in fuzzy control [1,[3][4][5][6][7][8][9][10]12,13,15,[17][18][19]21,26] and rough sets for target control [2,20,27,28]; again, by the combination of fuzzy sets (FS) theory and rough sets (RS) theory, i.e., fuzzy rough sets (FRS) theory [14,16,29], we can obtain a new control algorithm, which here is called the fuzzy rough (FR) control algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Research on two different mathematical theories on control

Wang

Pang

et al. 2008

Journal of Computational and Applied Mathematics

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With a brand-new theory, this paper not only provides the differences of attributes in concept, formula expression and function type between fuzzy rough sets and probability statistics, but also introduces their differences in algorithms on target control for better solving the control problem. Some new definitions and theorems concerning fuzzy rough sets and probability statistics are given, but this paper mainly makes a comparison of two control algorithms for the target tracking. The simulation results show that the comprehensive performance of the fuzzy rough sets algorithm is better than that of the probability statistics algorithm, but its control effect is not as good as that of the latter on multisensor target control. Finally, some problems concerning the combination of fuzzy rough sets and the probability statistics phenomenon to be solved and development trends are discussed. By these investigations, we can choose the optimal control algorithms for accomplishing better target control.

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Hierarchical adaptive Kalman filtering for interplanetary orbit determination

Cited by 14 publications

References 18 publications

Adaptive Filtering and Change Detection

Adaptive Filtering and Change Detection

Structurally adaptive modular networks for nonstationary environments

Research on two different mathematical theories on control

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