A distributed Kalman filtering algorithm with fast finite-time convergence for sensor networks

Wu, Zongze; Fu, Minyue; Xu, Yong; Lu, Renquan

doi:10.1016/j.automatica.2018.05.012

Cited by 74 publications

(32 citation statements)

References 39 publications

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“…From Table 3, we can see that the communication bandwidth and frequency requirement of Algorithm 6 is the lowest (or one of the lowest). n × n + n 1 ICF [13] n × n + n d FT-DKF [18] n…”

Section: Resultsmentioning

confidence: 99%

“…Diffusion-based algorithms exchange intermediate estimations between the neighborhoods of each node and calculate the final estimation by a convex combination of information from neighbors. In [17,18], the authors obtain the sum of global measurement information in finite communication cycles through the diffusion of measurement information and propose a finite-time distributed Kalman filter (FT-DKF). In [19], the authors extend the algorithm proposed in [17] to cyclic graphs.…”

Section: Introductionmentioning

confidence: 99%

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Distributed Kalman Filtering Based on the Non-Repeated Diffusion Strategy

Zhang

Shen

2020

Sensors

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Estimation accuracy is the core performance index of sensor networks. In this study, a kind of distributed Kalman filter based on the non-repeated diffusion strategy is proposed in order to improve the estimation accuracy of sensor networks. The algorithm is applied to the state estimation of distributed sensor networks. In this sensor network, each node only exchanges information with adjacent nodes. Compared with existing diffusion-based distributed Kalman filters, the algorithm in this study improves the estimation accuracy of the networks. Meanwhile, a single-target tracking simulation is performed to analyze and verify the performance of the algorithm. Finally, by discussion, it is proved that the algorithm exhibits good all-round performance, not only regarding estimation accuracy.

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“…From Table 3, we can see that the communication bandwidth and frequency requirement of Algorithm 6 is the lowest (or one of the lowest). n × n + n 1 ICF [13] n × n + n d FT-DKF [18] n…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distributed Kalman Filtering Based on the Non-Repeated Diffusion Strategy

Zhang

Shen

2020

Sensors

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“…A number of DKF algorithms have been presented in the literature [ 37 , 58 , 94 , 95 ]. In our solution, we implemented the diffusion-based DKF algorithm proposed by Battistelli et al [ 58 ] as a starting point to establish the importance of distributed data fusion in improving the leak detection accuracy.…”

Section: Methodsmentioning

confidence: 99%

A Distributed Computing Solution Based on Distributed Kalman Filter for Leak Detection in WSN-Based Water Pipeline Monitoring

Nkemeni

Mieyeville

Tsafack

2020

Sensors

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Wireless Sensor Network (WSN) applications that favor more local computations and less communication can contribute to solving the problem of high power consumption and performance issues plaguing most centralized WSN applications. In this study, we present a fully distributed solution, where leaks are detected in a water distribution network via only local collaborations between a sensor node and its close neighbors, without the need for long-distance transmissions via several hops to a centralized fusion center. A complete approach that includes the design, simulation, and physical measurements, showing how distributed computing implemented via a distributed Kalman filter improves the accuracy of leak detection and the power consumption is presented. The results from the physical implementation show that distributed data fusion increases the accuracy of leak detection while preserving WSN lifetime.

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“…Using (12), (13), (32), (34) and (36) and recalling that x(k + 1|k ),c(k + 1), and v 1 (k + 1) are mutually independent, the covariance matrix of the output error Pỹ 1ỹ1 (k + 1|k ) = E{ỹ 1 (k + 1|k )ỹ T 1 (k + 1|k )} is given by:…”

Section: The Recursive Formula Of the Filtered Estimatormentioning

confidence: 99%

A Generalized Decomposed State Estimation Approach for Uncertain Constrained Stochastic Nonlinear Systems

Hassan

2020

IEEE Access

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In this paper, a decomposed state estimator is developed for stochastic constrained nonlinear discrete-time dynamical systems with uncertain parameters. The proposed estimator can deal with general nonlinear uncertain stochastic systems without any pre-defined specifications on the system structure and/or the measurement model. Moreover, it can handle estimation problems for nonlinear stochastic systems subject to a set of imposed linear and/or nonlinear equality and/or inequality constraints. The mathematical structure of the proposed estimator is developed using multiple projection approach and its performance is analyzed and compared with the global (un-decomposed) structure. The main features of the proposed estimator are: it reduces the processing time, it can handle estimation problems with bad initial conditions of the estimator, it can be implemented on modern parallel processing facilities to further reduce the processing time, and last but not least, it minimizes the rounding off errors and hence improves the numerical stability of the algorithm. Simulations results on different real applications are presented to show the effectiveness of the developed approach, and its improved performance when compared with other techniques reported in the literature. INDEX TERMS Constrained stochastic uncertain nonlinear systems, decomposed state estimation, extended Kalman filter, large scale systems, performance analysis.

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A distributed Kalman filtering algorithm with fast finite-time convergence for sensor networks

Cited by 74 publications

References 39 publications

Distributed Kalman Filtering Based on the Non-Repeated Diffusion Strategy

Distributed Kalman Filtering Based on the Non-Repeated Diffusion Strategy

A Distributed Computing Solution Based on Distributed Kalman Filter for Leak Detection in WSN-Based Water Pipeline Monitoring

A Generalized Decomposed State Estimation Approach for Uncertain Constrained Stochastic Nonlinear Systems

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