Analysis of Optimal Reconstruction Methods Based on Incomplete Information From Sensor Nodes Using Kalman Filter

Huang, Yanqiu; Yu, Wanli; Usman, Muhammad; Wang, Shengdi; García-Ortiz, Alberto

doi:10.1109/jsen.2018.2849643

Cited by 4 publications

(3 citation statements)

References 28 publications

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“…P shows the order of the filter and has to be chosen optimally. The results achieved using the model described by Equation ( 4) and the associated topics can be read in [3,14]. The main problem associated with this model that it contains a limited amount of information for the compensation of loss measurement.…”

Section: Improved Compensation Using the Ar Model (Normal Equations)mentioning

confidence: 99%

“…Temporary defective sensors, limited bandwidth of communication lines, limited memory sizes, and mismatching of measurement devices, to name a few, are all possible causes of such loss. In order, to avoid the adverse effects arising due to loss of data in control and communication systems has been one of the most challenging research problems for the last few decades [3].…”

Section: Introductionmentioning

confidence: 99%

“…v m , u m , and w m are the system output data, input signal, and sensor noise, respectively. The variables o, p, and q in Equation (3) show the order of the past samples and are to be decided optimally [3].…”

mentioning

confidence: 99%

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Compensation of Data Loss Using ARMAX Model in State Estimation for Control and Communication Systems Applications

et al. 2021

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Compensation of data loss in the state estimation plays an indispensable role in efficient and stable control and communication systems. However, accurate compensation of data loss in the state estimation is extremely challenging issue. To cater this challenging issue, two techniques such as the open-loop Kalman filter and the compensating closed-loop Kalman filter have emerged. The closed-loop technique compensates for the missing data using the autoregressive model. However, the autoregressive model used only past measurements for data loss compensation. Considering only one parameter, i.e., the past measurements, is insufficient and leads to inaccurate state estimation. Thus, in this work, autoregressive moving average with exogenous inputs model considers three parameters, i.e., the past measurements, the input signal, and the sensor noise, simultaneously to compensate data loss in state estimation. To endorse the effectiveness and applicability of the proposed model, a standard mass-spring-damper is employed in the case study. Simulation results show that the proposed model outperforms the existing autoregressive models in terms of performance parameters.

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Section: Improved Compensation Using the Ar Model (Normal Equations)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%