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Based on the random finite set (RFS) framework and the probability hypothesis density (PHD) filter, the extended target PHD (ET‐PHD) filter is proposed for multiple extended target tracking. However, the clutter process in the ET‐PHD filter is modelled as Poisson RFS, which is reasonable for only some scenarios in reality. An easily implemented ET‐PHD filter for arbitrary clutter process, has not yet been studied. In this work, another form of the general PHD filter, suitable for arbitrary clutter and target measurement is derived using a probability generating functional (PGFL)‐based RFS framework. The proposed filter is equivalent to the general PHD filter proposed by Clark and Mahler, but it is easily implemented in an arbitrary clutter process because its clutter process is denoted by the probability density function. Then, an arbitrary clutter ET‐PHD (AC‐ET‐PHD) filter is simplified from the general PHD filter derived by us. To reduce the computational complexity of the proposed filter, a variant of the distance partitioning algorithm is put forward. Simulation results show that the AC‐ET‐PHD filter can be applied to the scenarios with the non‐Poisson clutter process, which means that it will be useful for multiple extended targets tracking in a more complicated clutter process. [ABSTRACT FROM AUTHOR]