The application of multi-robot collaborative localization and tracking in complex environments is becoming increasingly widespread. To enhance the localization accuracy and real-time performance of multi-robot systems in dynamic environments, this study proposes an Extended Kalman Filter-Evidential Particle Filter (EKF-EPF) algorithm based on Ultra-wideband (UWB) technology. This algorithm addresses nonlinear issues inherent in traditional localization methods and improves the system's accuracy and stability. Experimental results indicate that the EKF-EPF algorithm achieves a Root Mean Square Error (RMSE) of 0.60 m in localization accuracy, significantly lower than those of the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF). In terms of real-time performance, the EKF-EPF algorithm has a computational time of 14.2 s, markedly reduced compared to the Particle Filter (PF) algorithm, and a latency of 95 ms, demonstrating superior real-time responsiveness. Regarding system stability, the EKF-EPF algorithm attains a stability score of 9.5, significantly higher than other algorithms. The EKF-EPF algorithm exhibits outstanding accuracy and real-time performance in multi-robot collaborative localization and tracking tasks, making it suitable for dynamically changing complex environments. It effectively enhances localization accuracy while reducing computational complexity and system latency.