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.

Integrated Sensing and Communication (ISAC) represents an innovative paradigm for enhancing spectrum and hardware utilization for both sensing and communication. A specific type of ISAC, radar backscatter communication, involves low-power nodes embedding data onto radar signal reflections rather than generating new signals. However, existing radar backscatter techniques only facilitate uplink communication from the tag to the radar, neglecting downlink communication. This paper introduces BiScatter, an integrated radar backscatter communication and sensing system that enables simultaneous uplink and downlink backscatter communication, radar sensing, and backscatter localization. This is achieved through the design of chirp-slope-shift-keying modulation on top of Frequency Modulated Continuous Wave (FMCW) radars, complemented by passive differential circuitry at the backscatter tags for low-power decoding. BiScatter also presents a packet structure compatible with off-the-shelf radars that offer accurate data processing and synchronization between radar and tag. We prototype this backscatter network in both 9GHz and 24GHz, demonstrating its capability to extend across different frequency bands. Our evaluations demonstrate that BiScatter supports two-way backscatter communication with BER lower than 10-3 up to 7m range and centimeter-level tag localization accuracy on top of off-the-shelf FMCW radars. The presented approach significantly augments the versatility and efficiency of ISAC for low-power devices.

A hands-free (HF) lean-to-steer control concept that uses torso motions is demonstrated by navigating a virtual robotic mobility device based on a ball-based robotic (ballbot) wheelchair. A custom sensor system (i.e., Torso-dynamics Estimation System (TES)) was utilized to measure and convert the dynamics of the rider’s torso motions into commands to provide HF control of the robot. A simulation study was conducted to explore the efficacy of the HF controller compared to a traditional joystick (JS) controller, and whether there were differences in performance by manual wheelchair users (mWCUs), who may have reduced torso function, compared to able-bodied users (ABUs). Twenty test subjects (10 mWCUs +10 ABUs) used the subject-specific adjusted TES while wearing a virtual reality headset and were asked to navigate a virtual human rider on the ballbot through obstacle courses replicating seven indoor environment zones. Repeated measures MANOVA tests assessed performance metrics representing efficiency (i.e., number of collisions), effectiveness (i.e., completion time), comfort (i.e., NASA TLX scores), and robustness (i.e., index of performance). As expected, more challenging zones took longer to complete and resulted in more collisions. An interaction effect was observed such that ABUs had significantly more collisions using JS vs. HF control, while mWCUs had little difference with either interface. All subjects reported greater physical demand was needed for HF control than JS control; although, no users visibly showed or expressed fatigue or exhaustion when using HF control. In general, HF control performed as well as JS control, and mWC’s performed similarly to ABUs.