A MultiTask Motion Information Extraction Method Based on Range-Doppler Maps for Near-vertical Scenarios

Pulse Doppler radar provides all-weather operational capability and enables simultaneous acquisition of target range and velocity through Range-Doppler (RD) maps. In near-vertical flight scenarios, the geometric structure of RD maps implicitly encodes key platform motion parameters, including altitude, velocity, and pitch angle. However, these parameters are strongly coupled in the RD domain, making effective decoupling difficult for traditional signal-processing-based inversion methods, particularly under complex terrain and near-vertical incidence conditions. Although recent advances in deep learning have shown strong potential for motion information sensing, multitask learning in this context still faces challenges in achieving both real-time performance and high estimation accuracy. To address these issues, this study proposes a novel network architecture, termed RDMFNet, that performs multirepresentation information fusion via shared encoders and parallel decoders, along with a two-stage progressive training strategy to enhance parameter estimation accuracy. Experimental results show that RDMFNet achieves estimation errors of 14.447 m for altitude, 4.635 m/s for velocity, and 0.755° for pitch angle, demonstrating its effectiveness for high-precision, real-time perception.