Distributed Federated Kalman Filter Fusion Over Multi-Sensor Unreliable Networked Systems

Xing, Zirui; Xia, Yuanqing

doi:10.1109/tcsi.2016.2587728

Cited by 52 publications

(23 citation statements)

References 34 publications

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“…sensor networks have been available in the literature, including centralized fusion and distributed fusion, as well as measurement fusion and state fusion [9,10,11,12,13,14,15,16,17,18]. However, most of the results are based on the idea of batch fusion, that is, measurements or local estimates are fused all at a time at the fusion instant until all of them are available at the estimator, as illustrated in Fig.1(a).…”

Section: Various Results On Multi-sensor Fusion Estimation Formentioning

confidence: 99%

Sequential fusion estimation for clustered sensor networks

Zhang

Shi

2018

Automatica

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We consider multi-sensor fusion estimation for clustered sensor networks. Both sequential measurement fusion and state fusion estimation methods are presented. It is shown that the proposed sequential fusion estimation methods achieve the same performance as the batch fusion one, but are more convenient to deal with asynchronous or delayed data since they are able to handle the data that is available sequentially. Moreover, the sequential measurement fusion method has lower computational complexity than the conventional sequential Kalman estimation and the measurement augmentation methods, while the sequential state fusion method is shown to have lower computational complexity than the batch state fusion one. Simulations of a target tracking system are presented to demonstrate the effectiveness of the proposed results.

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Section: Various Results On Multi-sensor Fusion Estimation Formentioning

confidence: 99%

Sequential fusion estimation for clustered sensor networks

Zhang

Shi

2018

Automatica

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“…Substituting (48) into P c (k|k), where P c (k|k) = E{X c (k|k)X T c (k|k)}, we have the error covariance matrix for the state…”

Section: Discussionmentioning

confidence: 99%

“…The combined tracks at local platforms were transmitted to the fusion center and further fused there with a constructed global model [43]. There are basically two fusion architectures: centralized [44], [45] and distributed [46]- [48]. For the distributed fusion, which is also called as the state-vector or track fusion, a group of local Kalman filters are used in parallel to obtain individual sensor-based estimates and the distributed fusion formulae are then applied to yield an improved joint estimate.…”

Section: Introductionmentioning

confidence: 99%

Centralized Fusion Based on Interacting Multiple Model and Adaptive Kalman Filter for Target Tracking in Underwater Acoustic Sensor Networks

et al. 2019

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Underwater acoustic sensor networks (UASNs) play an important role in the ocean's protection. They can realize real-time data collection, monitoring, exploration, and many other underwater applications by connecting and coordinating seafloor sensors and underwater vehicles. To achieve these application objectives, such as fishes tracking in biological monitoring field and submarines tracking in military field, target tracking is one of the key techniques. This paper presents a centralized fusion algorithm based on the interacting multiple models and the adaptive Kalman filter (IMMCFAKF) for target tracking in UASNs. Specifically, by introducing an adaptive forgetting factor into the optimal centralized fusion Kalman filter algorithm, the optimal centralized fusion adaptive Kalman filter (CFAKF) algorithm is obtained first. Then, combining the superiorities of both the optimal CFAKF algorithm and the conventional IMM algorithm, the optimal IMMCFAKF is achieved. The numerical simulations are provided to demonstrate the effectiveness of the proposed optimal IMMCFAKF algorithm. INDEX TERMS Underwater acoustic sensor networks, target tracking, interacting multiple model, adaptive forgetting factor, optimal centralized fusion, Kalman filter. The associate editor coordinating the review of this manuscript and approving it for publication was Sammy Chan.

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“…With the communication delays, the dimensionality reduction fusion estimation must solve two challenging issues: one is how to compensate the information loss caused by the communication delays and bandwidth constraints under a unified mathematical model; The other one is how to fuse the asynchronous local compressed estimates because of communication delays. Notice that the centralized and distributed fusion estimation algorithms have been proposed in [23], [35]- [40] based on different communication delay models, however, the main results in [23], [35]- [40] cannot be extended to the case of the dimensionality reduction estimation with communication delays. The reason is that the data compression and information compensation in dimensionality reduction may change the property of the original measurements (e.g., the statistical correlation in [30], [32] has been changed under the Kalman fusion structure).…”

Section: A Related Workmentioning

confidence: 99%

“…where f i (t) is defined in (40). On the other hand, for i = j, it is concluded from the statistical property of H i (t) that…”

Section: A2: the Proof Of Lemmamentioning

confidence: 99%

Distributed Dimensionality Reduction Fusion Estimation for Cyber-Physical Systems Under DoS Attacks

Chen

Zhang

et al. 2019

IEEE Trans. Syst. Man Cybern, Syst.

147

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This paper studies the distributed dimensionality reduction fusion estimation problem with communication delays for a class of cyber-physical systems (CPSs). The raw measurements are preprocessed in each sink node to obtain the local optimal estimate (LOE) of a CPS, and the compressed LOE under dimensionality reduction encounters with communication delays during the transmission. Under this case, a mathematical model with compensation strategy is proposed to characterize the dimensionality reduction and communication delays. This model also has the property to reduce the information loss caused by the dimensionality reduction and delays. Based on this model, a recursive distributed Kalman fusion estimator (DKFE) is derived by optimal weighted fusion criterion in the linear minimum variance sense. A stability condition for the DKFE, which can be easily verified by the exiting software, is derived. In addition, this condition can guarantee that estimation error covariance matrix of the DKFE converges to the unique steady-state matrix for any initial values, and thus the steady-state DKFE (SDKFE) is given. Notice that the computational complexity of the SDKFE is much lower than that of the DKFE. Moreover, a probability selection criterion for determining the dimensionality reduction strategy is also presented to guarantee the stability of the DKFE. Two illustrative examples are given to show the advantage and effectiveness of the proposed methods. WVWHS 7U^; W`J J J J J WVWHS 7U^ ; W`J J J J J J Fig. 4. The estimation performances of the CSE (i.e., Tr{Ξ 11 (t)}) with different selection probabilities γ 11 .

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Distributed Federated Kalman Filter Fusion Over Multi-Sensor Unreliable Networked Systems

Cited by 52 publications

References 34 publications

Sequential fusion estimation for clustered sensor networks

Sequential fusion estimation for clustered sensor networks

Centralized Fusion Based on Interacting Multiple Model and Adaptive Kalman Filter for Target Tracking in Underwater Acoustic Sensor Networks

Distributed Dimensionality Reduction Fusion Estimation for Cyber-Physical Systems Under DoS Attacks

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