**5. Conclusions**

In this paper, a heuristic method called the refined PHD filter is proposed to improve the multi-target tracking performance of the PHD filter under low detection probability in practice. First, survival probability dependent on target state is defined, which is one of the conditions of performing posterior weights revision. Then, we label individual targets and particles, which can be utilized to confirm if miss detection occurs for each target and identify particles representing the undetected target. In addition, it can provide track-valued estimates of individual targets. When miss detection occurs due to low detection probability, posterior particle weights will be revised according to the prediction step. In order to distinguish real targets and false alarms in real time, we regard the target confirmation problem as a hypothesis test problem and introduce sequential probability ratio test to judge the success probability of the two-point distribution. Simulation results with respect to various detection probabilities, average numbers of false alarms and continuous miss detection durations are provided, which indicates that the multi-target tracking performance of the R-PHD filter outperforms the competing methods.

**Author Contributions:** Conceptualization, S.W.; Data curation, S.W.; Methodology, Q.B.; Project administration, Z.C.; Software, Q.B.; Validation, S.W.; Visualization, S.W.; Writing—original draft, S.W.; Writing—review & editing, Q.B.

**Funding:** This research received no external funding and the APC was funded by National Key Laboratory of Science and Technology on ATR.

**Acknowledgments:** The authors would like to thank the Editor and the anonymous reviewers for their valuable comments and suggestions.

**Conflicts of Interest:** The authors declare no conflict of interest.
