We also compare the proposed approach for the AVP system with already existing approaches based on radar and radar-IR fusion. The sensor location, in case of the AVP system based on radar, is set to be at [0, 0, 2 m]. The uncertainty levels in the target measurements of the radar are defined by the diagonal elements of the measurement covariance matrix, $RR$, and are quantified by standard deviations, $\sigma r=1\u2009\u2009m$, $\sigma \epsilon =26.4\u2009\u2009mrad$, and $\sigma \eta =26.4\u2009\u2009mrad$. The target has the same velocity while the initial position vector is determined by [0, 1000 m,2 m]. The sampling time is equal to that of the IR sensors, i.e, $T=0.01\u2009\u2009s$. A Kalman filter is used to estimate the position and velocity of the target by utilizing the unbiased converted measurements in Cartesian coordinates as described in Refs. ^{2} and ^{25}. In the next case where the radar measurements and the measurements of an IR sensor are fused, the radar location is not changed, but the IR sensor location is given by the vector [0.5 m, 0, 2 m]. The target position is also kept the same as in the case of the radar based system. The IR sensor used here shares the same specifications and statistical parameters as in the case of an IR only AVP system. The predicted target impact point in this case is the radar position. It is also assumed that both the sensor systems are perfectly synchronized. The Kalman filter is used to update the state estimate with the converted radar measurements whereas an EKF is used to update the IR information by utilizing the updated Kalman filter state estimates as the predicted state estimates.