A Concurrent Multibeam Passive Radar for Respiratory Detection From Multiple Subjects

• Published in: IEEE transactions on microwave theory and techniques Vol. 73; no. 8; pp. 4298 - 4311
• Main Authors: Huang, Shuaiming, Zhao, Heng, Hong, Hong, Guo, Fengxian, Peng, Mugen, Zhu, Xiaohua
• Format: Journal Article
• Language: English
• Published: IEEE 01.08.2025
• Subjects: Baseband; Digital receiver; Interference; Monitoring; multibeam passive radar; noncontact vital sign detection; Passive radar; Radar; Radar antennas; Radar detection; Sensors; Wireless fidelity; Wireless sensor networks
• ISSN: 0018-9480; 1557-9670
• DOI: 10.1109/TMTT.2025.3545123

The proliferation of the Internet of Things (IoT) technology has increased wireless interconnectivity, enhancing smart living environments. The millimeter-wave radar is prevalent for indoor positioning, and it consumes spectrum resources and risk interference in multiradar scenarios. In contrast, passive sensing leverages ambient electromagnetic signals, detecting targets through channel variations without occupying the spectrum. However, existing systems are constrained by hardware designs, thus resulting in limited environmental adaptability and poor sensing capabilities. In this article, a concurrent multibeam passive radar is proposed for localization and respiration detection of multiple human targets. Utilizing 16 independent digital receivers with a variable frequency local oscillator (LO), the radar adaptively senses ambient radio frequency signals and selects suitable ones for target detection. Upon identifying the positions of radiation sources and targets, the system generates multiple independent beams to capture motion, mitigating multipath echo interference from wide-beam antennas and enhancing environmental adaptability. Performance evaluation of the passive radar involved various experiments with simulated radiation sources, sliding platforms, and real human targets. The proposed passive radar system accurately localizes and distinguishes external illuminator from human targets, achieving an angular estimation error of sub-2° in 3 m. It also detects respiration signals through orthogonal baseband processing, with a maximum error of 0.2 beats per minute (BPM) and a median error below 0.03 BPM, ensuring high-precision respiratory monitoring.