Radio frequency fingerprint identification (RFFI) is a critical technique for ensuring the stability and security of Internet of Things (IoT) networks by exploiting unique hardware-induced characteristics of received signals. However, the extraction of hardware-induced characteristics from the transmitter is frequently obfuscated by the receivers’ hardware impairments. This challenge is particularly pronounced in low signal-to-noise ratio (SNR) conditions, where it significantly diminishes the accuracy of RFFI systems. To address this issue, we propose a novel receiver-agnostic RFFI approach to mitigate the impact of noise and enhance the extraction of transmitter features. The proposed approach uses demodulation and reconstruction techniques and employs adversarial training to achieve noise-robust feature extraction. An adaptive noise mitigation mechanism is designed to further reduce the interference resulting from noise. The results demonstrate that the accuracy of the proposed approach is 38.4% higher than the comparative methods at 0 dB SNR. Furthermore, a comparative experiment confirms the individual advantages of each component, validating the effectiveness of our proposed approach.