HIV Virus States Estimation by Extended Kalman Particle Filter

Due to the HIV prevalence, the problem of controlling and predicting the states and parameters of this disease has attracted many scholars and researchers. Because of the nonlinearity of the equations of this disease, to estimate its states, a Particle Filter has been applied which use a suitable resampling method. due to the importance of being accurate in estimating the states of this disease, the Extended Kalman Filter has been used in determining the optimal probable density function in a Particle Filter. In this paper, by combining a particle filter and an extended Kalman filter called EKPF, an attempt is made to estimate the status and parameters of the HIV equations. The simulation results confirm the accuracy of state estimating of the disease using the proposed Filter.