As radars become more prevalent as a sensing solution, detecting and profiling unknown radars become crucial for privacy or interference avoidance purposes. ChirpEye [3] is a radar detection system capable of identifying the parameters and angle of arrival (AoA) of Frequency-Modulated Continuous-Wave (FMCW) radars without prior knowledge of radar configuration or location. In this paper, we provide and extended analysis on robustness of ChirpEye under varying multipath conditions or highly mobile scenarios. First, we prove that Chirpeye can preserve its accurate radar detection and characterization even under high mobility due to the unique tag structure that cancels out Doppler shifts. Furthermore, through controlled simulations, we show that multipath interference could cause chirp slope and AoA estimation errors only under low SINR conditions and within a specific multipath delay range.

As Frequency-Modulated Continuous Wave (FMCW) radar systems become increasingly prevalent across various sensing applications, detecting their presence is crucial to mitigate interference and address potential security risks. Existing methods for spectrum sensing or detecting unintended Radio Frequency (RF) transmissions rely on expensive specialized hardware because these radars typically operate in the GHz frequency range and utilize large bandwidths. To overcome these limitations, we present ChirpEye, a simple but effective tag design that is capable of identifying FMCW radar waveforms without requiring prior knowledge of radar parameters such as chirp slope, operating frequency, or bandwidth. In addition, ChirpEye can identify the direction of incident signal, hinting at the potential location of the radar. The key innovation of ChirpEye lies in its novel tag design, which uses multiple antennas and delay lines to process GHz-level radar signals with only kHz sampling rates. The tag structure generates unique baseband frequencies that are proportional to FMCW waveform parameters. We also propose a new super-resolution algorithm, called Spectra-MUSIC, which can accurately estimate these beat frequencies from noisy data. Our extensive evaluations demonstrate that ChirpEye achieves 99% accuracy in detecting FMCW radars at distances up to 15 meters with less than 5% median error in estimating the radar chirp slope and less than 15 degrees median error in estimating the direction of the radar.

This paper introduces BiScatter2 as an extension of BiScatter [3], an integrated radar backscatter communication and sensing system. BiScatter2 provides simultaneous uplink and downlink communications, and precise radar-based sensing and localization. By refining the signal processing techniques and tag architecture design, BiScatter2 extends the operational range, setting a new baseline for two-way radar backscatter systems. This functionality is enabled through the use of chirp-slope-shift-keying modulation applied to Frequency Modulated Continuous Wave (FMCW) radars. BiScatter2 incorporates passive differential circuitry on backscatter tags for efficient, low-power decoding and extends its coverage range by combining the tag decoder and retro-reflective structure. Our evaluation results show an increase of 46% maximum range under the same throughput in downlink communication, which increases the original work's capability for commercial radars.

Recent regulations on monitoring infants and children in vehicle cabins have spurred interest in using Millimeter-wave (mmWave) radars due to their reliability in various lighting conditions and privacy benefits. However, existing radar-based vital sign detection solutions fail in car settings with abundant occlusions or closely-seated multi-person scenarios. To resolve these limitations, we introduce BSENSE, a joint occupancy and vital sign monitoring system using a single radar that is robust to occlusion and varying seating arrangements and number of occupants in vehicle cabins. BSENSE incorporates synthetic wireless reflectors positioned in car corners to redirect radar signals toward blind spots, enabling Non-Line-of-Sight (NLoS) vital sign detection while maintaining sensing performance in Line-of-Sight (LoS) areas. The proposed system employs a hybrid architecture combining signal processing and a deep learning pipeline that can detect the car seating layout and jointly learn occupied seats and signatures of breathing to distinguish adults from children and infants, and monitor their vital signs over time. Our extensive evaluations with 120,000 radar data points, 400 different experimental scenarios, a mix of 10 adults, 5 children of age 1--11, and two programmable infant and child simulators demonstrate BSENSE's capability in child detection with over 97% accuracy and estimating their breathing rate within 6 BPM error, even in multi-person and NLoS scenarios, and across different car models.

We demonstrate Platypus, a sub-millimeter micro-displacement sensing system presented in [3]. Micro-displacement measurement is a crucial task in industrial systems such as structural health monitoring, where millimeter-level displacement of specific points on the structure or machinery parts can jeopardize the integrity of the structure and potentially leading to catastrophic damage or collapse. Platypus enables sub-millimeter level sensing accuracy by using mmWave backscatter tags and their reflection as phase carriers to shift the phase changes due to tiny displacements to clean frequency bins for precise tracking. It then reconstructs the tag phase changes with sub-millimeter level accuracy even from extended ranges (over 100m) or in non-line-of-sight (NLoS) situations where the tag is blocked by other objects. Here, we demonstrate Platypus’s performance by attaching a Platypus tag to a stepper motor-driven motion-stage and demonstrating the micro-displacement detection in real time, and the system robustness against multipath and occlusions.

Micro-displacement measurement is a crucial task in industrial systems such as structural health monitoring, where millimeter-level displacement of specific points on the structure or machinery displace can jeopardize the integrity of the structure and potentially leading to catastrophic damage or collapse. Traditionally, such displacements on large structures are measured using visual sensing platforms or advanced surveying equipment. However, they either fall short in varying weather and lighting conditions or require installation and maintenance of high-power sensing platforms that are expensive to deploy at scale, especially if continuous measurements are desired.