Doppler-Division MIMO Radar-Based Target Detection for Edge Sensing Systems

Low-altitude platforms, such as unmanned aerial vehicles (UAVs), are increasingly employed for real-time sensing in edge-enabled internet of things (IoT) networks, where radar-based perception is critical for situational awareness. Multiple-input multiple-output (MIMO) radar systems offer high angular resolution and spatial diversity, making them well-suited for such applications. To fully exploit the benefits of MIMO, efficient waveform designs that support simultaneous multi-channel transmission are essential. Doppler division multiple access (DDMA) is a promising waveform strategy that achieves transmit orthogonality through Doppler-domain encoding, offering strong inter-channel orthogonality and supporting low-complexity implementation. However, in multi-target environments, DDMA-MIMO radar suffers from mutual shadowing effects that degrade detection performance. To address this challenge, this paper proposes a double-threshold detection framework tailored for DDMA-MIMO radar in edge sensing systems. The method employs a double-threshold strategy: an adaptive first threshold based on the Akaike information criterion (AIC) selects candidate targets, and a second threshold derived via maximum likelihood estimation produces the final detections. Theoretical analysis and simulation results demonstrate that the proposed double-threshold framework consistently outperforms conventional approaches, providing robust detection and effective false-alarm regulation.