Abstract
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.
Highlights
Academic Editor: Department of Electrical Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan; Department of Electrical Engineering, University of Engineering and Technology, Mardan 23200, Pakistan
Before introducing the data loss in the estimation process, we present the performance of conventional Kalman filtering
Data loss has been introduced for a period of 415 samples and is inserted from 2235 ms to 2650 ms
Summary
State estimation has been an interesting and active research area over the past decades due to its important role in systems where direct access to the measured state of system is either impossible or very difficult [1]. There are high demands on the design of robust algorithms that provide bounded estimation errors when a data loss occurs at the system output, such as when the measurements channels fail (sensors) [2] In both the control and communication literatures, data loss is an important case study. Considering past measurements and control input parameters is insufficient and leads to inaccurate state estimation With this motivation, in this paper, the autoregressive–movingaverage with exogenous inputs (ARMAX) model is developed to compensate date loss in state estimation for control and communication systems applications. The ARMAX model compensate for the lost signal and missing data by using an auxiliary vector employing the input to the system, the measurement values, and the sensor noise. Linear prediction theory has been used in integration with Kalman filtering, to deal with the problem of data loss in the state estimation.
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