The Joint Probabilistic Data Association (JPDA) algorithm needs accurate dynamic model of the target when solving the problem of multi-target tracking.However, the dynamic model mismatch often occurs in tracking multiple maneuvering targets.As one of the effective solutions, the Strong Tracking Filter (STF) is designed for a single maneuvering target in an environment without clutter.In order to improve the tracking accuracy of multiple maneuvering targets in clutter environment, a Joint Probabilistic Data Association and Strong Tracking Filter (JPDA-STF) algorithm is proposed.In order to obtain the fading factor of each target, the algorithm uses the weighted fusion of the measurement which is associated with the target to obtain the target innovation covariance, and the measurement weight is calculated by JPDA.The state prediction covariance can be obtained through the fading factor, and then the target state can be updated under the Kalman filter framework.The experimental results show that this algorithm has higher tracking accuracy than the JPDA.