State estimation in computer virus epidemic dynamical systems using hybrid extended Kalman filter

Lei, Chengwei; Qian, Chunjiang; Tian, Weisong; Jia, Rong

doi:10.1109/icinfa.2017.8078932

Cited by 2 publications

(1 citation statement)

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“…In this section, to check the results of our proposed algorithm , the a novel and simple proposed algorithm is compared with standard Kalman Filter [16] and Average Smoothed Filter. We use the continuous time dynamics system [17] with state-dependent transitions to model the aerial vehicle to observe the performance of our algorithms. This efficient state estimation will help us to implement the effective estimation algorithm for multiple modes hybrid system under the noisy state measurement scenario.…”

Section: Simulation Resultsmentioning

confidence: 99%

A Simple And Novel Algorithm For State Estimation Of Continuous Time Linear Stochastic Dynamic Systems Excited By Random Inputs

Paul¹,

Thakar²,

Patel³

2019

IJITEE

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The major goal of this paper is to explore the effective state estimation algorithm for continuous time dynamic system under the lossy environment without increasing the complexity of hardware realization. Though the existing methods of state estimation of continuous time system provides effective estimation with data loss, the real time hardware realization is difficult due to the complexity and multiple processing. Kalman Filter and Particle Filer are fundamental algorithms for state estimation of any linear and non-linear system respectively, but both have its limitation. The approach adopted here, detect the expected state value and covariance, existed by random input at each stage and filtered the noisy measurement and replace it with predicted modified value for the effective state estimation. To demonstrate the performance of the results, the continuous time dynamics of position of the Aerial Vehicle is used with proposed algorithm under the lossy measurements scenario and compared with standard Kalman filter and smoothed filter. The results show that the proposed method can effectively estimate the position of Aerial Vehicle compared to standard Kalman and smoothed filter under the non-reliable sensor measurements with less hardware realization complexity.

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Section: Simulation Resultsmentioning

confidence: 99%