Two implementations of marginal distribution Bayes filter for nonlinear Gaussian models

Liu, Zongxiang; Li, Lijuan; Xie, Weixin; Li, Liang-qun

doi:10.1016/j.aeue.2015.05.007

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Bias and Variation Of Parameter Estimation For Nonlinear Model1

Introduction1

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Cited by 9 publications

(2 citation statements)

References 14 publications

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“…Obviously, the second-order expansion ofṠ(β) based on the true parameter value β can be obtained according to Taylor formula as Eq. (19).…”

Section: Bias and Variation Of Parameter Estimation For Nonlinear Modelmentioning

confidence: 99%

Optimal Weight and Parameter Estimation of Multi‐structure and Unequal‐Precision Data Fusion

Wang

Zhou

et al. 2017

Chin. j. electron.

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“…Obviously, the second-order expansion ofṠ(β) based on the true parameter value β can be obtained according to Taylor formula as Eq. (19).…”

Section: Bias and Variation Of Parameter Estimation For Nonlinear Modelmentioning

confidence: 99%

Optimal Weight and Parameter Estimation of Multi‐structure and Unequal‐Precision Data Fusion

Wang

Zhou

et al. 2017

Chin. j. electron.

View full text Add to dashboard Cite

“…Track association, the prerequisite for track fusion, is a key procedure in MTT. [5][6][7][8] Hence, it is necessary to improve the performance of track association in processing rate and fusion results in accuracy. In DSNs, each local node utilizes sensor tracks to generate local tracks.…”

Section: Introductionmentioning

confidence: 99%

Weighted fuzzy track association method based on Dempster–Shafer theory in distributed sensor networks

Fan

Cao³

et al. 2016

International Journal of Distributed Sensor Networks

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The uncertainty problem in sensor track to local track association is a difficult problem in distributed sensor networks, particularly when there is a big difference of sensors' tracking performance. To solve this problem, a weighted fuzzy track association (FTA) method based on Dempster-Shafer theory is proposed. In the proposed method, five characteristics of sensor tracks from different sensors are established, and meanwhile their belief functions are defined to determine the corresponding beliefs. Considering the different effects of sensor tracks on track association, the reliabilities of sensor tracks are further presented and their magnitudes can be calculated by the combination belief function defined. Then, these reliabilities are used to reconstruct the fuzzy association degrees by the FTA method. The proposed method has an advantage that it can dynamically allocate the weight of each sensor track in association decision according to its characteristics. The performance of the proposed method is evaluated by using two experiments with simulation data in manoeuvring and uniform situations. It is found to be better than those of other two track association methods in tracking accuracy.

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Tracking multiple maneuvering targets using a sequential multiple target Bayes filter with jump Markov system models

Liu

Zhang

et al. 2016

Neurocomputing

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Two implementations of marginal distribution Bayes filter for nonlinear Gaussian models

Cited by 9 publications

References 14 publications

Optimal Weight and Parameter Estimation of Multi‐structure and Unequal‐Precision Data Fusion

Optimal Weight and Parameter Estimation of Multi‐structure and Unequal‐Precision Data Fusion

Weighted fuzzy track association method based on Dempster–Shafer theory in distributed sensor networks

Tracking multiple maneuvering targets using a sequential multiple target Bayes filter with jump Markov system models

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