A Digital Beamforming Fast-Start Passive Radar for Indoor Motion Detection and Angle Estimation

As wireless sensing and communications technologies continue to advance, the limited bandwidth in the microwave and millimeter-wave spectra has sparked new research in improved spectrum efficiency. Microwave radar sensors have seen considerable use as a noncontact sensor but contribute to spectrum congestion when many radar sensors are placed in one environment. Furthermore, traditional radar sensors pose security concerns since their illumination signals can be detected or spoofed. Passive sensing has demonstrated the potential to detect millimeter-scale (mm-scale) motions using ambient signals, but recent works have so far been limited to one- or two-channel systems incapable of determining the angle of a target with sufficient resolution. In addition, noise and clutter effects on passive sensing and beamforming models have not yet been considered, which creates performance limitations in passive sensing when no signals originate onboard. This article proposes a passive radar beamforming architecture that can sense and simultaneously determine a target’s angle using 16 digital beamforming channels and a third-party microwave transmitter. The proposed architecture leverages a custom radio frequency front end to downconvert received signals to a baseband and a fast-startup baseband amplifier to allow for rapid measurements of low-frequency signals after powering the radar. The theory of passive radar beamforming is provided along with noise and clutter models to model the performance of beamforming passive sensing systems more accurately. The design procedure for the radio frequency front end and beamforming array are provided to ensure adequate performance of the final array. The experimental results show that the radar is capable of accurately measuring the angle of a cooperative transmitter in a controlled environment, as well as the respiration frequency and angle of a human target.

I. Introduction

Microwave radar sensing uses the phase delay and Doppler frequency shift of traveling electromagnetic (EM) waves to detect target parameters such as distance and motion wirelessly [1]. Many radars that are used today leverage active radar architectures, meaning that the sensor transmits a dedicated signal to illuminate a target and compares the reflected waves to the known reference. Based on these architectures, microwave radar has been used in many applications such as vital-sign detection [2], [3], [4], gesture recognition [5], automotive sensing [6], [7], [8], and smart-infrastructure technologies [9], [10]. Furthermore, as the Internet of Things (IoT), autonomous vehicles, and smart healthcare technologies continue to advance, the number of deployed radar sensors continues increasing to address a variety of applications in day-to-day life [11]. As a result, active radar can experience difficulties when scaling due to interference issues among sensors within the same area. This becomes especially true as the EM spectrum becomes more congested, and engineers begin focusing on advanced techniques to share and minimize spectrum usage. In addition, active radar systems can create security concerns since the radar’s illumination signal can be detected and spoofed to confuse the radar sensor [12].