In multi-sensor integrated navigation, extensive observation information and low sampling efficiency will lead to poor navigation performance. An adaptive iterated particle filter is presented and applied in global positioning system/inertial navigation system integrated navigation. Firstly, importance density function is updated iteratively by the particle filter itself. Secondly, by using a simulated annealing algorithm with an adaptive annealing parameter, the current measurement can be quickly incorporated into the sampling process, resulting in greatly improved sampling efficiency. Finally, the performance of the proposed method is examined on the sea trail and the simulation experiment, respectively. The results show that the method can provide better navigation accuracy and improve performance of the filter.