As Location-Based Services (LBS) for smartphones flourish, pedestrian navigation leveraging their integrated sensors has attracted significant interest. To further promote the overall performance of smartphone PDR/GNSS navigation and positioning accuracy, this paper proposes an Augmented Measurement-based PDR/GNSS Kalman Filter (AMKF) pedestrian navigation method. First, the GNSS-estimated velocity and heading are introduced as additional observations into the Kalman filter. Then, time synchronization is achieved using the GNSS timestamp as the reference, and the GNSS velocity is utilized to estimate the stride length. The augmented measurements are then effectively fused with the positional data, as well as the stride length and heading information derived from the PDR algorithm. Experimental results indicate a noteworthy 21.1% decrease in average horizontal positioning error in contrast to the traditional Kalman filter method, which solely relies on GNSS position as observation. The proposed AMKF method effectively boosts the precision of the integrated PDR/GNSS pedestrian navigation system.