The perception of autonomous vehicles using radars has attracted increased research interest due its ability to operate in fog and bad weather. However, training radar models is hindered by the cost and difficulty of annotating largescale radar data. To overcome this bottleneck, we propose a self-supervised learning framework to leverage the large amount of unlabeled radar data to pre-train radar only embeddings for self-driving perception tasks. The proposed method combines radar-to-radar and radar-to-vision contrastive losses to learn a general representation from unlabeled radar heatmaps paired with their corresponding camera images. When used for downstream object detection, we demonstrate that the proposed self-supervision framework can improve the accuracy of state-of-the-art supervised baselines by 5.8% in mAP. Code is available at https://github.com/yiduohao/Radical.

We present the design, implementation, and evaluation of RFlect, a mmWave imaging system capable of producing around-the-corner high-resolution images in practical environments. RFlect leverages signals reflected off complex surfaces (e.g., poles, concave surfaces, or composition of multiple surfaces) to image objects that are not in the RF line-of-sight. RFlect models the reflections and introduces reconstruction algorithms for different types of surfaces. It also leverages a novel method for precisely mapping the location and geometry of the reflecting surface. We also derive the theoretical resolution and coverage for different reflecting surface geometries. We built a prototype of RFlect and performed extensive evaluations to demonstrate its ability to reconstruct the shape of objects around the corner, with an average Chamfer Distance of 2cm and 3D F-Score of 88.6%.

Using millimeter-wave radars as a perception sensor provides self-driving cars with robust sensing capability in adverse weather. However, mmWave radars currently lack sufficient spatial resolution for semantic scene understanding. This paper introduces Radatron++, a system leverages cascaded MIMO (Multiple-Input Multiple-Output) radar to achieve accurate vehicle detection for self-driving cars. We develop a novel hybrid radar processing and deep learning approach to leverage the 10× finer angular resolution while combating unique challenges of cascaded MIMO radars. We train and evaluate Radatron++ with a novel cascaded radar dataset. Radatron++ achieves 93.9% and 58.5% Average Precisions with 0.5 and 0.75 Intersection over Union thresholds respectively in 2D bounding box detection, outperforming prior work using low-resolution radars by 9.3% and 18.1% respectively.

This paper introduces Wireless IoT-based Noise Cancellation (WINC) which defines a framework for leveraging a wireless network of IoT microphones to enhance active noise cancellation in noise-canceling headphones. The IoT microphones forward ambient noise to the headphone over the wireless link which travels a million times faster than sound and gives the headphone a future lookahead into the incoming noise. While leveraging wireless lookahead has been explored in past work, prior systems are limited to a single noise source. WINC, however, can simultaneously cancel multiple noise sources by using a network of IoT nodes. Scaling wireless lookahead aware noise cancellation is non-trivial because the computational and protocol delays can defeat the purpose of leveraging wireless lookahead. WINC introduces a novel algorithm that operates in the frequency domain to efficiently cancel multiple noise sources. We implement and evaluate WINC to show that it can cancel three noise sources and outperforms past work and state-of-the-art headphones without requiring completely blocking the users’ ears.

This paper demonstrates high-resolution imaging using millimeter Wave (mmWave) radars that can function even in dense fog. We leverage the fact that mmWave signals have favorable propagation characteristics in low visibility conditions, unlike optical sensors like cameras and LiDARs which cannot penetrate through dense fog. Millimeter-wave radars, however, suffer from very low resolution, specularity, and noise artifacts. We introduce HawkEye, a system that leverages a cGAN architecture to recover high-frequency shapes from raw low-resolution mmWave heat-maps. We propose a novel design that addresses challenges specific to the structure and nature of the radar signals involved. We also develop a data synthesizer to aid with large-scale dataset generation for training. We implement our system on a custom-built mmWave radar platform and demonstrate performance improvement over both standard mmWave radars and other competitive baselines.

This paper proposes a new over-the-air (OTA) calibration method for millimeter wave phased arrays. Our method leverages the channel estimation process which is a fundamental part of any wireless communication system. By performing the channel estimation while changing the phase of an antenna element, the phase response of the element can be estimated. The relative phase of the phased array can also be obtained by collecting all the estimated phase responses with a shared reference state. Hence, the phase mismatches of the phased array can be resolved. Unlike prior work, our calibration method embraces all the array components such as power-divider, phase shifter, amplifier and antenna and thus, spans the full chain. By overriding channel estimation, our proposed technique does not require any additional circuits for calibration. Furthermore, the calibration can be performed online without the need to pause the communication. We tested our method on an eight element phased array at 24GHz which we designed and fabricated in PCB for verification. The measured beam patterns prove the viability of our proposed method.

Ear devices, such as noise-canceling headphones and hearing aids, have dramatically changed the way we listen to the outside world. We re-envision this area by combining wireless communication with acoustics. The core idea is to scatter IoT devices in the environment that listen to ambient sound and forward it over their wireless radio. Since wireless signals travel much faster than sound, the ear-device receives the sound much earlier than its actual arrival. This "glimpse" into the future allows sufficient time for acoustic digital processing, serving as a valuable opportunity for various signal processing and machine learning applications. We believe this will enable a digital app store around human ears.

Active Noise Cancellation (ANC) is a classical area where noise in the environment is canceled by producing anti-noise signals near the human ears (e.g., in Bose's noise cancellation headphones). This paper brings IoT to active noise cancellation by combining wireless communication with acoustics. The core idea is to place an IoT device in the environment that listens to ambient sounds and forwards the sound over its wireless radio. Since wireless signals travel much faster than sound, our ear-device receives the sound in advance of its actual arrival. This serves as a glimpse into the future, that we call lookahead, and proves crucial for real-time noise cancellation, especially for unpredictable, wide-band sounds like music and speech. Using custom IoT hardware, as well as lookahead-aware cancellation algorithms, we demonstrate MUTE, a fully functional noise cancellation prototype that outperforms Bose's latest ANC headphone. Importantly, our design does not need to block the ear - the ear canal remains open, making it comfortable (and healthier) for continuous use.