Abstract: The increased measurement noise, resulting from strong clutter and fluctuations in the radar cross section (RCS) of the target, adversely affects the accuracy of target angle measurement. In the traditional method, the observation data of individual target is filtered and then used for ballistic extrapolation. However, when significant measurement errors exist, the filtering process converges slowly and estimation errors become unacceptable, making it difficult to meet the high-precision positioning requirements of artillery locating & calibration radars.To address this problem, a filtering algorithm based on multi-trajectory joint measurement (MTLF) is proposed in this paper. The MTLF algorithm replaces single-shot measurement results with fused measurement results from multiple projectiles in the filtering equation, which can reduce filtering posterior estimation errors and improve the consistency of extrapolation. To validate the effectiveness of the algorithm, multiple sets of data for projectiles under various shooting conditions are generated using a simulator. A comparative analysis is performed between the proposed method and the traditional method in terms of measurement error, filtering error, and positioning accuracy. Results show that compared with traditional method, the proposed method effectively suppresses measurement errors, improves filtering accuracy, and reduces the circular error probable(CEP) of the artillery group, demonstrating strong practical value.