Intentional spoofing of Global Navigation Satellite Systems (GNSS) signals has become a pervasive threat, particularly in conflict regions. This talk will present spoofing detection and mitigation techniques leveraging Doppler-frequency characteristics and IMU sensors. In spoofing scenarios where authentic and spoofing signals coexist, it is challenging to distinguish the authentic signals across all channels. To address this, this talk will introduce the Receiver Motion-Induced Doppler Frequency Difference (RMIDFD), designed to eliminate the effects of receiver clock drift-induced Doppler shifts and satellite movement-induced Doppler shifts, retaining only the motion-induced Doppler components. The RMIDFD thus preserves only the kinematic signature associated with either the authentic or the spoofed trajectory. Whenever the legitimate receiver trajectory and the spoofed trajectory exhibit distinct motion, the GNSS signals corresponding to each can be effectively discriminated.  The true movement type of the receiver is obtained with the assistance of an IMU sensor through a neural network.  In addition, it supports the effective recovery of position in the presence of spoofing attacks. Simulations demonstrate the detection and mitigation process and validate the corrected positioning results in a Digital Twin platform. The proposed algorithm is further implemented in a GNSS software-defined radio (SDR) testbed and the hardware experiments confirm the successful spoofing detection and mitigation.