Distributed Kalman filtering for sensor network with balanced topology

Summary This article considers a distributed Kalman filtering problem for linear system contaminated by complex multi‐channel random uncertain parameter in which a number of nodes cooperative without central coordination to estimate a common state based on local measurements and data received from neighbors. We propose an approach to eliminate this error propagation. The proposed local filters are guaranteed to be stable under some mild conditions on certain global structural data, and their fusion yields the centralized Kalman estimate. Then, we extend this method to smoothing and deconvolution algorithm. Finally, simulation experiments demonstrate the validity of the proposed approach.

show abstract

“…This leads to

X_{t | t}^{i} = X_{t | t - 1}^{i} + \sum_{t | t}^{i} true(ψ_{t}^{i} - Ψ_{t}^{i} X_{t | t - 1}^{i} true) + Θ_{t} \sum_{j \in 𝒩_{i}} true(X_{t | t - 1}^{j} - X_{t | t - 1}^{i} true)

where matrix

Θ_{t}

is a free parameter that needs to be designed. In particular, the choice

Θ_{t} = I - \sum_{t | t}^{i} Ψ_{t}^{i}

is implicitly made in Reference 47.…”

Section: Overview Of Available Distributed Methodsmentioning

confidence: 99%

Distributed Kalman filter for linear system with complex multi‐channel stochastic uncertain parameter and decoupled local filters

PuShi

YuXingkai

LiJianxun

2021

Adaptive Control & Signal

View full text Add to dashboard Cite

show abstract

“…In order to further improve the detection performance of the fusion system, a series of new optimal fusion algorithms based on covariance, large deviation analysis, least square fusion rules, and Rao test [8][9][10][11][12] of the distributed detection fusion system are proposed. In recent years, many scholars have introduced a neural network [13], Kalman filter [14][15][16], and (generalized) likelihood ratio (GLRT) [17][18][19][20] into sensor systems to realize signal detection in various fields. All the above researches assume that the noise obeys a certain distribution and lack the research on chaotic noise background combined with phase space reconstruction.…”

Section: Introductionmentioning

confidence: 99%

Distributed Sensor Local Linear Fusion Detection of Weak Pulse Signal in Chaotic Background

Zhao

et al. 2021

Journal of Sensors

View full text Add to dashboard Cite

This paper combines the distributed sensor fusion system with the signal detection under chaotic noise to realize the distributed sensor fusion detection from chaotic background. First, based on the short-term predictability of the chaotic signal and its sensitivity to small interference, the phase space reconstruction of the observation signal of each sensor is carried out. Second, the distributed sensor local linear autoregressive (DS-LLAR) model is constructed to obtain the one-step prediction error of each sensor. Then, we construct a Bayesian risk model and also obtain the corresponding conditional probability density function under each sensor’s hypothesis test which firstly needs to fit the distribution of prediction errors according to the parameter estimation. Finally, the fusion optimization algorithm is designed based on the Bayesian fusion criterion, and the optimal decision rule of each sensor and the optimal fusion rule of the fusion center are jointly solved to effectively detect the weak pulse signal in the observation signal. Simulation experiments show that the proposed method which used a distributed sensor combined with a local linear model can effectively detect weak pulse signals from chaotic background.

show abstract

“…At present, there is an increasing number of researchers that recognize the importance of the DF problem over SNs (Bu et al, 2019(Bu et al, , 2018He et al, 2020;Liu et al, 2019;Wang et al, 2020;Yang et al, 2019). Some typical research directions include that the distributed H ∞ filtering (Dong et al, 2013;Han, Wang, Chen et al, 2021;Qu et al, 2019;Shen et al, 2010Shen et al, , 2011, and the distributed Kalman filtering (Ji et al, 2017;Li, Dong et al, 2019;Wang et al, 2019;Wu et al, 2018;Yang et al, 2020), and distributed set-membership filtering (Liu et al, 2019;Ma et al, 2017). As for DF problems, each node has its own filter through the available information from both itself and its neighbouring sensor nodes.…”

Section: Introductionmentioning

confidence: 99%

Event-triggered distributed filtering for discrete-time systems with integral measurements

Cong

Han

et al. 2021

Systems Science & Control Engineering

View full text Add to dashboard Cite

This paper handles the event-triggered distributed filtering problem for a class of discrete-time systems with integral measurements over sensor networks (SNs). First of all, a plant is monitored by an SN formulated by a digraph. The integral measurement phenomenon is modelled for each node to account for the interval time taken by sample collection. Next, to mitigate the communication burden of network, an event generator function is introduced for each node to regulate the data transmission to its neighbouring nodes. This paper aims to find the suitable filter gains for each node such that the filtering error dynamics satisfies the exponentially ultimately boundedness. Via the Lyapunov stability theory, a sufficient condition is established which ensures the existence of the distributed filter under the desirable performance index. Moreover, the ultimate bound in the performance index is minimized via the robust optimization method. Then, the filter gains are acquired by solving a linear matrix inequality with the YALMIP toolbox. Finally, a numerical simulation example is employed to verify the usefulness of the distributed filtering algorithm developed in this paper.

show abstract

Distributed Kalman filtering for sensor network with balanced topology

Cited by 31 publications

References 23 publications

Distributed Kalman filter for linear system with complex multi‐channel stochastic uncertain parameter and decoupled local filters

Distributed Kalman filter for linear system with complex multi‐channel stochastic uncertain parameter and decoupled local filters

Distributed Sensor Local Linear Fusion Detection of Weak Pulse Signal in Chaotic Background

Event-triggered distributed filtering for discrete-time systems with integral measurements

Contact Info

Product

Resources

About