The integration of communication and sensing functions within mmWave systems has gained attention due to the potential for enhanced passive sensing and improved communication reliability. State-of-the-art techniques separate these two functions in frequency, use of hardware, or time, i.e., sending known preambles for channel sensing or unknown symbols for communications. In this paper, we introduce Panoptic, a novel system architecture for integrated communication and sensing sharing the same hardware, frequency, and time resources. Panoptic jointly detects unknown symbols and channel components from data-modulated signals. The core idea is a new beam manipulation technique, which we call compressive sidelobe forming, that maintains a directional mainlobe toward the intended communication nodes while acquiring unique spatial information through pseudorandom sidelobe perturbations. We implemented Panoptic on 60 GHz mmWave radios and conducted extensive over-the-air experiments. Our results show that Panoptic achieves reflector angular localization error of less than 2° while at the same time supporting mmWave data communication with a negligible BER penalty when compared with conventional communication-only mmWave systems.

mmWave antenna array calibration is a necessary yet tedious and costly process in manufacturing to capture the non-idealities in phased arrays, in order to obtain codebooks for accurate and stable beam steering. Unfortunately, predefined codebooks provided by manufacturers to steer beams in a given set of directions do not support the arbitrary beam shapes required for various mmWave communication, sensing, and security applications. To create arbitrary beam patterns, one needs to first find the unknown calibration vector for the particular phased array in use. In this paper, we introduce EiCal, a novel zero-shot technique that leverages the beamforming codebook advertised by the manufacturer to extract the calibration vector at zero cost (i.e., with no additional measurements). The key idea is that the unknown desired calibration vector can be obtained via an appropriately designed eigen-decomposition of the given codebook. We experimentally demonstrate the efficacy of EiCal on a 60 GHz mmWave array for two scenarios: angle estimation using compressive pseudorandom beams, and simultaneous steering of beams and nulls. Our results also point to potential simplifications in the calibration process at the manufacturer.

Millimeter-wave (mmWave) radar is increasingly being considered as an alternative to optical sensors for robotic primitives like simultaneous localization and mapping (SLAM). While mmWave radar overcomes some limitations of optical sensors, such as occlusions, poor lighting conditions, and privacy concerns, it also faces unique challenges, such as missed obstacles due to specular reflections or fake objects due to multipath. To address these challenges, we propose Radarize, a self-contained SLAM pipeline that uses only a commodity single-chip mmWave radar. Our radar-native approach uses techniques such as Doppler shift-based odometry and multipath artifact suppression to improve performance. We evaluate our method on a large dataset of 146 trajectories spanning 4 buildings and mounted on 3 different platforms, totaling approximately 4.7 Km of travel distance. Our results show that our method outperforms state-of-the-art radar and radar-inertial approaches by approximately 5x in terms of odometry and 8x in terms of end-to-end SLAM, as measured by absolute trajectory error (ATE), without the need for additional sensors such as IMUs or wheel encoders.