To solve the problem of low tracking accuracy when fusing different sensorsan Improved Adaptive Unscented Kalman Filter (IAUKF) algorithm using weighted data fusion is proposed.In the processing of fusing different sensorsscene switching will cause decline in sensor accuracy.Through introducing the idea of Sage-Husa adaptive filteringdifferent weights are set for data from different sensorsand the statistical characteristics of measurement noise are processed in real time.Joint Probabilistic Data Association (JPDA) is used to remove clutter and associate measurement with target trajectories.This algorithm is used to track multiple aerial targets in the modified spherical coordinate system.The simulation results show that the new algorithm effectively reduces state estimation errors and improves tracking accuracy in comparison with the corresponding method based on the standard UKF algorithm.