Robust distributedH∞filtering over an uncertain sensor network with multiple fading measurements and varying sensor delays

Hedayati, Mohammad; Rahmani, M.

doi:10.1002/rnc.4779

Cited by 20 publications

(23 citation statements)

References 52 publications

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“…Over the past decade, multi‐sensor systems have been extensively studied due to their broad applications in biological systems, signal processing, control, robotics, reconnaissance, and environmental applications 11‐13 . Consensus is one of the basic problems in multi‐sensor systems.…”

Section: Introductionmentioning

confidence: 99%

“…Over the past decade, multi-sensor systems have been extensively studied due to their broad applications in biological systems, signal processing, control, robotics, reconnaissance, and environmental applications. [11][12][13] Consensus is one of the basic problems in multi-sensor systems. A consensus algorithm determines the exchange of information between all neighbors in a network and results in reaching an agreement on a certain quantity or common value in all sensors.…”

Section: Introductionmentioning

confidence: 99%

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Consensus‐based robust least‐squares filter for multi‐sensor systems

Roshan

Rahmani²

2022

Adaptive Control & Signal

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In this article, a consensus-based robust regularized least-squares filter is designed for multi-sensor systems with norm-bounded uncertainties. In this approach, a min-max optimization problem is presented based on a consensus protocol on estimates. The advantage of a consensus filter is that each node estimates its local states, in addition to reaching an agreement on estimates made by all sensors on the network. By introducing appropriate conversions the proposed optimization problem is converted to a robust regularized least-squares problem.Solving this problem results in the structure of the proposed filter. Then, the recursive formulation of the filter is obtained in measurement form and information form. Finally, in order to investigate the efficacy, good proficiency, and robustness of the proposed consensus-based robust least-squares filter, it has been applied to an uncertain multi-sensor system with 100 nodes and its results have been compared with existing consensus filters.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Consensus‐based robust least‐squares filter for multi‐sensor systems

Roshan

Rahmani²

2022

Adaptive Control & Signal

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“…In actual systems, the system cannot meet these conditions due to some uncertainties, making the Kalman filter lose its optimality, which reduces the estimation accuracy and even leads to divergence. Researchers have introduced the idea of robust control in filtering to solve this problem, thus forming a robust estimation [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods

Jin

Jeremiah

et al. 2021

Sensors

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State estimation is widely used in various automated systems, including IoT systems, unmanned systems, robots, etc. In traditional state estimation, measurement data are instantaneous and processed in real time. With modern systems’ development, sensors can obtain more and more signals and store them. Therefore, how to use these measurement big data to improve the performance of state estimation has become a hot research issue in this field. This paper reviews the development of state estimation and future development trends. First, we review the model-based state estimation methods, including the Kalman filter, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF), cubature Kalman filter (CKF), etc. Particle filters and Gaussian mixture filters that can handle mixed Gaussian noise are discussed, too. These methods have high requirements for models, while it is not easy to obtain accurate system models in practice. The emergence of robust filters, the interacting multiple model (IMM), and adaptive filters are also mentioned here. Secondly, the current research status of data-driven state estimation methods is introduced based on network learning. Finally, the main research results for hybrid filters obtained in recent years are summarized and discussed, which combine model-based methods and data-driven methods. This paper is based on state estimation research results and provides a more detailed overview of model-driven, data-driven, and hybrid-driven approaches. The main algorithm of each method is provided so that beginners can have a clearer understanding. Additionally, it discusses the future development trends for researchers in state estimation.

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“…These filters are not sensitive to the noises' statistic specifications. In the

H_{\infty}

filter design, the estimation error dynamic is stable and

H_{\infty}

norm of the filtering system that is the induced

L_{2}

gain from noise to estimation error is bounded [18–24]. The

H_{2}

filtering problem is interpreted as the minimization of the

H_{2}

norm of the transfer function from the process noise to the estimation error [25–27].…”

Section: Introductionmentioning

confidence: 99%

Optimal Lp filtering for discrete‐time non‐Gaussian dynamic systems

Javanfar

Rahmani

2021

IET Control Theory & Appl

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This paper investigates the Lp filtering problem for linear dynamic systems. The main objective is to discuss the optimal filter for non‐Gaussian systems. The filter structure is obtained by extension of the maximum a posteriori estimation problem for general norm exponential probability density functions. The obtained filter has a linear structure, and two different algorithms are proposed for 1≤p<∞ and p=∞. In the following, the choice of p has been discussed based on the asymptotic distribution and the central limit theorem as well as the kurtosis of error probability density function. Both of them confirm that the L2 filter is optimal for Gaussian systems, and the Lp filter, for p<2, has the best performance for the systems with leptokurtic error distributions, and for p>2, it is the best for the systems with platykurtic error distributions. In this regard, it is shown that the L1 and L∞ filters have the best MSE performance for the systems with Laplacian and uniform error probability distribution functions, respectively. Simulation results verify the superior performance of the proposed filtering approach for linear time‐invariant and time‐varying systems.

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Robust distributedH_∞filtering over an uncertain sensor network with multiple fading measurements and varying sensor delays

Cited by 20 publications

References 52 publications

Consensus‐based robust least‐squares filter for multi‐sensor systems

Consensus‐based robust least‐squares filter for multi‐sensor systems

The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods

Optimal Lp filtering for discrete‐time non‐Gaussian dynamic systems

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