The paper is devoted to propose a data fusion algorithms into multistatic radar network to improve its tracking capability. The proposed data fusion algorithm is based on using common measurement architecture gives state estimates with relatively low and medium uncertainty followed by cumulative measurement fusion (CMF) or cumulative state vector fusion (CSVF) algorithm which is very simple, easy to implement and can be used in real time. Extended Kalman Filter (EKF) is used as a non-linear tracking and predictor algorithm. The system is simulated using Matlab program to compare the performance of the estimation routines of both fusion algorithms and the targets scenario is simulated using Monte Carlo simulation. Simulation results have shown that these cumulative fusion algorithms improve the multistatic radar network tracking capability and produce a significant reduction in the root sum square error (RSSE), absolute error, and root sum square variance (RSSV) than achieved from monostatic radar.