Distributed filtering for time-varying networked systems with sensor gain degradation and energy constraint: a centralized finite-time communication protocol scheme

Wang

et al. 2023

IEEE Trans. Ind. Inf.

This paper addresses the particle filtering problem for a class of nonlinear/non-Gaussian systems with quantized measurements and multiple degrading sensors. A degradation variable described by the Wiener process is proposed to describe the phenomenon of sensor degradation that is often encountered in engineering practice. The measurement output of each sensor is quantized by a uniform quantizer before being sent to the remote filter. An augmented system is constructed which aggregates the original system state and the degradation variables. In the presence of the sensor degradation and the quantization errors, a new likelihood function at the remote filter is calculated by resorting to all the transmitted measurements. According to the mathematical characterization of the likelihood function, a novel particle filtering algorithm is developed where the parameters of both the degradation processes and the quantization functions are exploited to obtain the modified importance weights. Finally, the effectiveness of the proposed method is shown via a target tracking example with bearing measurements.

Section: Introductionmentioning

confidence: 99%

A Novel Algorithm for Quantized Particle Filtering With Multiple Degrading Sensors: Degradation Estimation and Target Tracking

Wang

et al. 2023

IEEE Trans. Ind. Inf.

Intl J Robust & Nonlinear

“…One of the key SN‐related research topics is distributed filtering that has proven to possess advantages in simplicity, efficiency, robustness, and flexibility over the conventional centralized algorithms 5‐8 . Distributed filtering methodologies have attracted a great deal of attention and some widely investigated schemes include distributed Kalman filtering, 9‐14 distributed H ∞ filtering, 15‐22 distributed fusion filtering, 23‐25 and distributed set‐membership filtering 26,27 …”

Section: Introductionmentioning

confidence: 99%

“…It is of significance to examine the impact of protocols on various filtering issues especially in the context of SNs with respect to their topologies. To date, much effort has been devoted to the investigation of filtering and control problems subject to different types of protocols such as Round‐Robin protocol (RRP), 16,31‐34 weighted try‐once‐discard protocol, 31 and stochastic communication protocol 11,35 . In particular, the well‐known RRP (also named as time‐division multiple access protocol or token ring protocol) has been extensively used in various filtering/estimation problems.…”

Section: Introductionmentioning

confidence: 99%

Scalable consensus filtering for uncertain systems over sensor networks with Round‐Robin protocol

Han

Wang

Chen

et al. 2020

This article is concerned with the scalable distributed H∞‐consensus filtering problem for a class of discrete time‐varying systems over sensor networks with the Round‐Robin protocol (RRP). The challenge comes from the fact that the time‐varying parameters of the network are subject to randomly occurring norm‐bounded uncertainties and the measurement outputs of the sensor nodes are saturated due to the sector nonlinearities. For preventing data collisions and saving energy, the RRP determines which neighboring node can access the shared network for information transmission at each time step. An H∞ performance index is proposed to characterize the disturbance attenuation level of the resulting filtering error dynamics. By stochastic analysis in combination with the recursive matrix inequality approach, a distributed filtering algorithm is developed for each individual sensor node to ensure the prespecified estimation performance. Finally, an illustrative simulation example is shown to verify the effectiveness and applicability of the theoretical results.

Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics

“…For this reason, the study of the estimation problem in this kind of systems with one or several network-induced uncertainties has become a hot research topic over the last years (see e.g. (Gao and Chen, 2014), (Chen et al, 2015), (Tian et al, 2016), (Caballero-Águila et al, 2017), (Zhao et al, 2018), (Liu et al, 2018) and a https://orcid.org/0000-0001-7659-7649 b https://orcid.org/0000-0001-8120-2162 c https://orcid.org/0000-0002-6853-555X (Yang et al, 2019)).…”

Section: Introductionmentioning

confidence: 99%

Signal Estimation with Random Parameter Matrices and Time-correlated Measurement Noises

Caballero-Águila

Hermoso-Carazo

Linares-Pérez

2019

This paper is concerned with the least-squares linear estimation problem for a class of discrete-time networked systems whose measurements are perturbed by random parameter matrices and time-correlated additive noise, without requiring a full knowledge of the state-space model generating the signal process, but only information about its mean and covariance functions. Assuming that the measurement additive noise is the output of a known linear system driven by white noise, the time-differencing method is used to remove this time-correlated noise and recursive algorithms for the linear filtering and fixed-point smoothing estimators are obtained by an innovation approach. These estimators are optimal in the least-squares sense and, consequently, their accuracy is evaluated by the estimation error covariance matrices, for which recursive formulas are also deduced. The proposed algorithms are easily implementable, as it is shown in the computer simulation example, where they are applied to estimate a signal from measured outputs which, besides including time-correlated additive noise, are affected by the missing measurement phenomenon and multiplicative noise (random uncertainties that can be covered by the current model with random parameter matrices). The computer simulations also illustrate the behaviour of the filtering estimators for different values of the missing measurement probability.