Abstract: ‍ ‍Multi-target tracking technology can simultaneously estimate the states and quantities of targets in complex tracking environments where the number of targets is unknown and missed detections， clutter， and noise exist. This technology has been widely applied in fields such as airborne early warning， autonomous driving， and mobile robotics， with significant application value in both defense and civilian technologies. However， in practical tracking environments， external interference and sensor instability can lead to outliers in measurement noise， exhibiting heavy-tailed characteristics. Additionally， the inaccurate motion models of targets in cluttered environments can generate heavy-tailed process noise. Continuing multi-target filtering under the Gaussian assumption in such scenarios significantly reduces tracking accuracy. A common solution to addressing this issue is to model heavy-tailed process and measurement noise as Student’s t-distributions and use them to correct standard multi-target filters under the random finite set （RFS） theory， thereby ensuring that the tracking performance does not diverge. However， multi-target tracking methods based on the RFS theory often incur substantial computational costs， resulting in increased system latency. This study proposes a robust multi-target tracking algorithm based on belief propagation （BP）， leveraging its strong scalability and low computational complexity. The algorithm first approximates the posterior probability density functions of each target as Student’s t-distribution mixture models， then recursively updates them through BP iterations， and finally estimates target states based on decision thresholds. Simulation experiments demonstrate that， compared to existing algorithms， the proposed algorithm achieves robust and effective tracking performance in scenarios with heavy-tailed processes and measurement noise.