To overcome the problems of accuracy degradation and divergence of the iterative extended Kalman filter (IEKF) when applying target tracking to model mismatches and noise time-variations, an adaptive IEKF algorithm based on multiple fading factors is proposed. First, a limited memory innovation covariance estimator based on the normal distribution is used to calculate the estimated value of innovation covariance and the multiple fading factors are distributed to each filtering channel according to the estimated covariance. Then, the filtering anomaly according to the χ 2 test principle is determined and the fading factors are introduced only when the system is abnormal. Finally, the radial distance and azimuth between the target and the observing station are used to determine the adaptive control of the IEKF iteration number. The simulation results show that compared with the traditional IEKF, when the system model is mismatched, the mean estimation error of position, velocity, and acceleration of the proposed algorithm is reduced by 86.97%, 33.18%, and 15.56%, respectively. When the process noise is time-varying, it is reduced by 60.35%, 18.42%, and 6.02%, respectively. When the measurement noise is time-varying, it is reduced by 50.60%, 18.78%, and 5.41%, respectively. Therefore, the proposed algorithm effectively improves filtering accuracy and robustness.