The development of a Block Modeler using parts with parametric definitions for the shipbuilding automation (SICE-ICCAS 2006)

Kim, Byung-su; Kim, Eun-Jung; Park, Jin-hyung; Park, Youngjun

doi:10.1109/sice.2006.315211

Cited by 1 publication

(3 citation statements)

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“…x we can apply the optimal SWKF to the subsystem (4) [9][10][11][12]. We obtain the following differential equations:…”

Section: Applied Mechanics and Materials Vols 446-447mentioning

confidence: 99%

“…Among them, the sliding window technique has been popular and successful, robust against temporal uncertainty, and thus rigorously investigated. It has been a general rule that LSWKFs are typically more robust against dynamic model uncertainties and numerical errors than standard local Kalman filters, which utilize all measurements [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, decentralized sliding window fusion filtering for multiple sensors with equal sliding window time intervals (sliding window sizes) has been proposed in [13]. In this case, all LSWKFs with the same sliding window time interval, which are fused, utilize finite measurements over the most recent time interval [9]- [12].…”

Section: Introductionmentioning

confidence: 99%

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Decentralized Fusion Filtering with Different Sensor Memories in Dynamics System with Uncertainties

Song

Huh²,

Shin

2013

AMM

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This paper is concerned with the decentralized sliding window Kalman filter for multisensor discrete-time dynamic linear systems having different sensor memories. The proposed filtering is presented that combines the Kalman filter and sliding window strategy. A decentralized fusion with the weighted sum structure is applied to the local sliding window Kalman filters (LSWKFs) having different sensor memory size. The proposed decentralized algorithm has a parallel structure and allows parallel processing of observations, thereby it more reliable than the centralized version if some sensors become faulty. Moreover, the choice of sliding window strategy makes the proposed algorithm robust against dynamic model uncertainties. The derivation of the error cross-covariances between the LSWKFs is the key idea of this paper. The application of the proposed decentralized filer to linear discrete-time dynamic systems within a multisensor environment demonstrates its high accuracy and computational efficiency.

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“…x we can apply the optimal SWKF to the subsystem (4) [9][10][11][12]. We obtain the following differential equations:…”

Section: Applied Mechanics and Materials Vols 446-447mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Decentralized Fusion Filtering with Different Sensor Memories in Dynamics System with Uncertainties

Song

Huh²,

Shin

2013

AMM

View full text Add to dashboard Cite

show abstract

The development of a Block Modeler using parts with parametric definitions for the shipbuilding automation (SICE-ICCAS 2006)

Cited by 1 publication

References 3 publications

Decentralized Fusion Filtering with Different Sensor Memories in Dynamics System with Uncertainties

Decentralized Fusion Filtering with Different Sensor Memories in Dynamics System with Uncertainties

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