The extended target probability hypothesis density (BET-PHD) method based on binominal distribution has shown better tracking performance than the Poisson ET-PHD method. However, the detection probability and measurement number maximum as prior information in BET-PHD are unknown in practical applications. Significant mismatches in these parameters make the method’s performance decline sharply. In view of the method to estimate the measurement number maximum already being presented by some literature, this paper proposes a beta Gaussian ET-PHD (BG-ET-PHD) filter for online estimating the detection probability. Firstly, use the conjugate prior, beta distribution of binominal distribution to estimate the detection probability, and then the BG-ET-PHD is obtained by combing beta distribution with BET-PHD. Finally, simulated results show that the BG-ET-PHD filter has good estimates for the detection probability and can obtain better tracking performance compared with gamma Gaussian ET-PHD (GG-ET-PHD) based on a Poisson model.