Abstract: An improved strong tracking filter algorithm combined with the unscented Kalman filter algorithm based on high order probability density derivative (HUKF) is proposed to solve the problem of decreasing the accuracy of the traditional unscented Kalman filter (UKF) in the system state mutation and high order strong tracking UKF(HSUKF) is established. The algorithm uses the extreme value of the high order derivative of Gauss’s probability density function as the Sigma sample for unscented transformation conversion and improves the approximate accuracy of nonlinear transformation by capturing the center moment of higher order by the sample point. The improved strong tracking filter algorithm is introduced into the HUKF, the adaptability of the algorithm in the case of state mutation is improved on the premise of not increasing the computational complexity by using the fading factor to correct the prediction of the covariance of new interest and the prediction of mutual covariance matrix, forcing new interest to be orthogonal. The algorithm is applied to the passive tracking of time difference of arrival/frequency difference of arrival. The HSUKF algorithm has the characteristics of low computational complexity and high estimation precision by the numerical simulation and example demonstration of the tracking problem of the mutation of the state of the target and it shows good filtering performance in the case of sudden change in the system state.