Unified optimal linear estimation fusion. II. Discussions and examples

Li, X.R.; Wang, Jie

doi:10.1109/ific.2000.862452

Cited by 13 publications

(12 citation statements)

References 4 publications

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“…extended Kalman filtering (EKF), unscented Kalman filtering (UKF), and particle filtering (PF), which involve state estimation using a set of local filters that communicate with all other nodes (see e.g. Li & Wang, 2000;Mutambara, 1998;Vercauteren & Wang, 2005, and the references therein). The information flow in the traditional decentralized Kalman filtering (see e.g.…”

Section: Tracking Methods For Peer-to-peer Networkmentioning

confidence: 99%

See 1 more Smart Citation

Target Tracking in Wireless Sensor Networks

Li¹,

Zhou²

2010

Wireless Sensor Networks: Application-Centric Design

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Section: Tracking Methods For Peer-to-peer Networkmentioning

confidence: 99%

“…However, the algorithms developed are free of energy and communication constraints, see e.g. Sun & Deng, 2004;Li & Wang, 2000;Zhou & Li, 2008a;Zhou & Li, 2008b. Novel fusion approaches include practical constraints in WSNs while keeping high fusion performance must be investigated (Ruan et al, 2008).…”

Section: Concluding Remarks and Open Research Directionsmentioning

confidence: 99%

Target Tracking in Wireless Sensor Networks

Li¹,

Zhou²

2010

Wireless Sensor Networks: Application-Centric Design

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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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“…Other related publications cited in the Table 2.7 are [338]- [339], [337], [332], [333,334], [37], [46], [47], [48], [48], [60], [59,58], [57], [56], [55], [54], [53], [52]. Other related publications cited in the Table 2.8 are [67], [68], [91], [93,94], [95], [115], [116], [117], [124], [125], [127] and [128].…”

Section: Msdf Systemsmentioning

confidence: 99%

Distributed Kalman Filtering

Khalid¹

2014

Networked Filtering and Fusion in Wireless Sensor Networks

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Unified optimal linear estimation fusion. II. Discussions and examples

Cited by 13 publications

References 4 publications

Target Tracking in Wireless Sensor Networks

Target Tracking in Wireless Sensor Networks

Estimation Fusion for Markovian Jump Linear System via Data Transformation

Distributed Kalman Filtering

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