As the promising and efficient approximation of the Bayesian filter for multi-target tracking, the probability hypothesis density (PHD) filter iteratively propagates the first-order statistical moment of the multi-target states other than the multi-target density. However, the PHD filter cannot cope with the pseudo missed-detection problem caused by the improper position distribution of target-originated measurements in scenarios. To address the problem, we propose a multi-target filtering algorithm by integrating a missed-detection renovation scheme and an improved component fusion scheme into the PHD filter. Specifically, the PHD filter is used to estimate time-varying number of targets and their states, and the missed-detection renovation scheme is used to redistribute the PHD of the pseudo missed detections from the multi-target posterior PHD. In addition, the improved component fusion scheme is used to reduce and optimize the components of targets in multi-target posterior PHD. Experiment results demonstrate that the proposed algorithm can achieve better estimation accuracy and reliability in possible pseudo missed-detection tracking scenarios when compared against the related existing multi-target PHD filters.