Optimal linear estimation fusion-part VII: dynamic systems

Li, X.R.

doi:10.1109/icif.2003.177482

Cited by 38 publications

(20 citation statements)

References 6 publications

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“…References [14], [15] studied distributed fusion with compressed data and [16], [1] addressed distributed fusion with transformed data. The series of papers [2,14,[17][18][19][20][21] presented several unified optimal linear fusion rules and established a general framework for estimation fusion. All these methods assume a single-model system.…”

Section: Introductionmentioning

confidence: 99%

Estimation Fusion for Markovian Jump Linear System via Data Transformation

Gao

Duan

2014

IEEE Trans. Aerosp. Electron. Syst.

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The work presented here deals with distributed estimation fusion for a Markovian jump linear system (MJLS) via a specific linear transformation of local measurements from [1]. Due to the possible singularity associated with this transformation, a relative likelihood of the system model given the transformed data is defined first. With this, distributed fusion for an MJLS is proposed. With full-rate communication, the distributed fusion and the centralized fusion (CF) have the same performance. To accommodate limited communication bandwidth, an extension to the reduced-rate communication case is also discussed. Two schemes to generate the transformed data are considered. In the first one, local sensors transform raw measurements directly. This is applicable to the situation where local sensors have very poor computational power. In the second scheme, transformed data are recovered from local single-model-based estimates indirectly. It is applicable to the Manuscript situation in which local estimates are desired and multiple model estimation cannot be afforded. Illustrative numerical results are provided to show the performance of the proposed fusion methods.

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Section: Introductionmentioning

confidence: 99%

Estimation Fusion for Markovian Jump Linear System via Data Transformation

Gao

Duan

2014

IEEE Trans. Aerosp. Electron. Syst.

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“…The proposed FF aims to fuse information from nonlinear models with cross-correlated noises, so a suitable nonlinear filter should be used as the local filter of FF instead of the KF. Compared with other classical nonlinear filters, the high degree cubature Kalman filter (HCKF) seems to be a good choice to be the substituted local filter, which has superior performance to the extend Kalman filter (EKF), the cubature Kalman filter (CKF), the unscented Kalman filter (UKF), and the particle filter (PF) 2 Mathematical Problems in Engineering [9][10][11][12]. To the best knowledge of the authors' knowledge, no works about the high degree cubature federated filters with correlated noises (HCFF-CNs) have been reported.…”

Section: Introductionmentioning

confidence: 99%

High Degree Cubature Federated Filter for Multisensor Information Fusion with Correlated Noises

Wang

2016

Mathematical Problems in Engineering

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This paper proposes an improved high degree cubature federated filter for the nonlinear fusion system with cross-correlation between process and measurement noises at the same time using the fifth-degree cubature rule and the decorrelated principle in its local filters. The master filter of the federated filter adopts the no-reset mode to fuse local estimates of local filters to generate a global estimate according to the scalar weighted rule. The air-traffic maneuvering target tracking simulations are performed between the proposed filter and the fifth-degree cubature federated filter. Simulations results demonstrate that the proposed filter not only can achieve almost the same accuracy as the fifth-degree cubature federated filter with independent white noises, but also has superior performance to the fifth-degree cubature federated filter while the noises are cross-correlated at the same time.

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“…This is often the case when each local tracker uses a Kalman filter to recursively estimate the target state. The state prediction at the fusion center can also be used as the prior if the fusion center assumes a kinematic model to propagate the target state [13]. However, the prediction error is also correlated with the state estimation error of each local tracker.…”

Section: Introductionmentioning

confidence: 99%

Optimal linear estimation fusion-part VII: dynamic systems

Abstract: -

Cited by 38 publications

References 6 publications

Estimation Fusion for Markovian Jump Linear System via Data Transformation

Estimation Fusion for Markovian Jump Linear System via Data Transformation

High Degree Cubature Federated Filter for Multisensor Information Fusion with Correlated Noises

Bayesian model selection for multisensor track-to-track association and track fusion

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