Noninvasive fetal cardiac monitoring is hampered by the poor quality of the fetal electrical signal and the mismatch between electrical and mechanical acquisition modalities. In this work, we propose a multimodal deep learning model for high-resolution fetal Doppler profile reconstruction, integrating multichannel abdominal ECG and degraded pulsed Doppler. The architecture consists of two encoders dedicated to the electrical and mechanical components, followed by a gated fusion mechanism that dynamically learns the contribution of each modality. The model is optimized using a hybrid loss that combines mean square error and Pearson correlation to preserve both numerical accuracy and morphological consistency of the signal. Results on the NINFEA dataset show that the multimodal approach outperforms unimodal configurations, achieving a Pearson correlation of 0.78. The explainability analysis highlights that the model focuses on the fetal QRS complexes and the systolic phases of the Doppler signal, suggesting the learning of the physiological electro-mechanical relationship.