Random target number, heavy clutter, and heavy target density make difficult for multiple targets tracking. Gaussian mixture Cardinalized probability hypothesis density (GM-CPHD) filter based on theory of finite set statistics can perform this tracking task while with complex computation. It is well known that the computation of square-root Kalman filter based on LDL decomposition is about the half of the one in extend Kalman filter (EKF). Hence, this paper design a multitarget tracking algorithm based on LDL decomposition which called LDL-GM-CPHD filter. Numerical example is given in this manuscript and the simulation results reveal that LDL-GM-CPHD filter can save about 30% time resource with similar tracking performance.