In this paper, based on Poisson point processes, two new methods for joint nonhomogeneous clutter background estimation and multitarget tracking are presented. In many scenarios, after the signal detection process, measurement points provided by the sensor (e.g., sonar, infrared sensor, radar) are not distributed uniformly in the surveillance region as assumed by most tracking algorithms. On the other hand, in order to obtain accurate results, the target tracking filter requires information about clutter's spatial intensity. Thus, nonhomogeneous clutter spatial intensity has to be estimated from the measurement set and the tracking filter's output. Also, in order to take advantage of existing tracking algorithms, it is desirable for the clutter estimation method to be integrated into the tracker itself. Nonhomogeneous Poisson point processes, whose intensity function are assumed to be a mixture of Gaussian functions, are used to model clutter points here. Based on this model, a recursive maximum likelihood (ML) method and an approximated Bayesian method are proposed to estimate the nonhomogeneous clutter spatial intensity. Both clutter estimation methods are integrated into the probability hypothesis density (PHD) filter, which itself also uses the Poisson point process assumption. The mean and the covariance of each Gaussian function are estimated and used to calculate the clutter density in the update equation of the PHD filter. Simulation results show that both methods are able to improve the performance of the PHD filter in the presence of slowly time-varying nonhomogeneous clutter background.