Traditional laser-based localization methods face target identification strangeness due to the lack of semantic information from LiDAR. To address this issue, this paper proposes a target localization method that integrates ultra-wideband (UWB) ranging with laser scanning information. First, the collected laser data is clustered, and the minimum Euclidean distance between clusters at adjacent time steps is used as the association criterion. The nearest neighbor data association algorithm is then employed to construct laser cluster trajectories, which are further matched with UWB ranging sequences to achieve motion target identification and tracking. Then, depending on whether the target is within the robot's field of view, the particle weights are updated during the particle filter update stage using either the similarity matching results or the UWB ranging, leading to the estimation of the target's position. The feasibility of the algorithm is validated in a 12 m×12 m indoor environment, showing that the proposed method can achieve a localization accuracy of 0.16 m while meeting real-time requirement.