Table 1 shows the simulation results of lifetime estimation using the MN-NLLS method,8–10 fast fitting method,15 MLE method initialized with ground truth,16 and our algorithm. Mean and standard deviation are as shown in Table 1 as calculated from 500 realizations of the Poisson random variable. The accuracy of algorithms decreases as the scaling Poisson variance factor decreases. As shown in Table 1, the MN-NLLS and fast fitting methods are quite sensitive to noise: the estimated mean value significantly deviates from true lifetime, especially when . In comparison, the proposed method is robust to noise: the relative error for the 2 ns component is 33%, 37.5%, 7%, and 7.5% for NM-NLLS, fast fitting, MLE, and the proposed method. The proposed method provides similar results the as MLE method initialized with the ground truth, since the proposed algorithm is based on MLE. However, the MLE method is not practical since the initial solution must be the ground truth. Otherwise, it falls in a local minimum, which is quite different from the desired solution.