To address the challenge of degraded positioning accuracy and reliability in smartphone global navigation satellite system (GNSS) signals caused by multipath and non-line-of-sight effects in complex environments such as urban canyons and dense high-rise areas, this paper proposes a resilient graph optimization-based global navigation satellite system/pedestrian dead reckoning (GNSS/PDR) autonomous navigation method within a multi-source integrated navigation system framework. Focusing on system detectability and reconfigurability, the approach designs an elastic fault-tolerant architecture that incorporates state-adaptive fault detection and gradient descent regression-based autonomous reconfiguration, aiming to enhance the robustness of the navigation system under dynamically changing environmental conditions. In terms of detectability, a state-correlated dynamic fault detection mechanism is introduced. When no fault is detected in the previous epoch, a sliding-window-based dynamic 3σ statistical detection method is applied. When a fault is detected in the previous epoch, a dynamic threshold strategy based on exponentially weighted moving average is employed for continuous anomaly monitoring. In terms of reconfigurability, once a fault is identified, the system performs fault diagnosis and autonomous reconfiguration using a gradient descent regression-based GNSS/PDR algorithm. The reconfiguration process first utilizes historical innovations to predict the system state, then performs magnitude correction based on the dynamic relationship between abnormal and repaired innovations, and finally achieves dynamic recovery of abnormal observations. Experimental results demonstrate that the proposed method reduces the average positioning error by more than 20% compared to traditional extend Kalman filter (EKF), Huber-based M-estimation EKF, factor graph optimization (FGO), and Huber-based M-estimation FGO algorithms. These findings indicate that the proposed method offers significant advantages in enhancing the positioning accuracy and robustness of smartphone-based pedestrian navigation under challenging multipath and NLOS environments, providing a valuable reference for the development of high-precision positioning applications in future consumergrade devices.