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ISSN E 2409-2770
ISSN P 2521-2419

Performance Evaluation of Data Loss Recovery Techniques using Compensated Closed Loop and Open Loop Kalman Filter


Khushal Khan, Abdul Qadar, Abdul Hadi


Vol. 9, Issue 01, PP. 17-21, January 2022

DOI

Keywords: KF(Kalmanfilter),AR(Autoregressive),ARMA( Auto-regressive moving Average),ARMAX(Auto-regressive moving average exogeneous)

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State estimates play a dominant role in almost all fields of engineering and technology, unambiguously. It plays a great role in many physical systems, where system measurements are uncertain. Instead, there are the highest requirements for the development of powerful algorithms that can lead to limited evaluation errors when the loss of the package occurs at the exit of the system, say, e.g., Data loss is the major issue of control and different areas of engineering which degrade the efficiency of the system[1-2]. The minimum mean square formula is used to minimize the error that occurred. Kalman filter is an iterative process it predicts the system state and will update its state after each step. Kalman filter is mostly used in challenging problems of data loss to overcome the effects of loss therefore it updates after estimating the actual state, infrequently it is quite challenging if the input is lost for a known duration of time. Data loss in the systems states is quite challenging and degrade the efficiency of communication and control systems. The most dominant method for the recovery of lost samples in the case of estimating the state of the system are OLKF and CLKF[3-4]. The CCLKF utilize the 3 strategies, Normal Equation, LDA, and LGA, using AR model, ARMA, and ARMAX(Auto regressive moving average with exogenous input) If the input is lost for a known time The effective technique is AR(where only previous measurements are used), ARMA (previous measurements and moving average), ARMAX(modal has more parameters for executing data loss i.e input, the noise we consider its results is best ) The accuracy of ARMAX must be higher than ARMA and AR Model but this technique is computationally expensive so there must be a trade-off between precision and simulation time In many systems the parameters increase the accuracy of the system increases but computational time also increase. Computation of this extra information bears an observable increase in Computational time. It will be verified after simulation that ARMAX will recover more efficiently as compared to AR and ARMA because the model parameter will increase in the case of   (ARMAX) model (i.e exogenous input, noise, and regression to previous data) It is considered that ARMA model is Efficient as compere to AR but computationally expensive to overcome the problem of efficiency and computational time we will evaluate the linear prediction coefficients of ARMAX model and compere the results with AR, ARMA and ARMAX model using open-loop and compensated closed loop Kalman filter.     


  1. Khushal Khan Khan, Khushalengr143@gmail.com, University of Engineering and Technology Peshawar, Pakistan.
  2. Abdul Qadar Qdar, abdulqadirpk7@hrbeu.edu.cn, Harbin Engineering University, China.
  3. Abdul Hadi Hadi, hadi33259@gmail.com, Harbin Engineering University, China.

Khushal Khan Abdul Qadar Abdul Hadi Performance Evaluation of Data Loss Recovery Techniques using Compensated Closed Loop International Journal of Engineering Works Vol. 9 Issue 01 PP. 17-21 January 2022 https://doi.org/10.34259/ijew.22.9011721.


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