This paper presents Millimetro, an ultra-low-power tag that can be localized at high accuracy over extended distances. We develop Millimetro in the context of autonomous driving to efficiently localize roadside infrastructure such as lane markers and road signs, even if obscured from view, where visual sensing fails. While RF-based localization offers a natural solution, current systems struggle operate accurately ranges under strict latency requirements. addresses this challenge by re-using...

This paper explores a machine learning approach on data from single-chip mmWave radar for generating high resolution point clouds – key sensing primitive robotic applications such as mapping, odometry and localization. Unlike lidar vision-based systems, can operate in harsh environments see through occlusions like smoke, fog, dust. Unfortunately, current processing techniques offer poor spatial compared to clouds. presents RadarHD, an end-to-end neural network that constructs lidar-like low...

Tire wear is a leading cause of automobile accidents globally. Beyond safety, tire affects performance and an important metric that decides replacement, one the biggest maintenance expense global trucking industry. We believe it to measure monitor in all automobiles. Current approach manual extremely tedious. Embedding sensor electronics tires challenging, given inhospitable temperature, pressure dynamics tire. Further, off-tire sensors placed well such as laser range-finders are vulnerable...

Tag localization is crucial for many context-aware and automation applications in smart homes, retail stores, or warehouses. While custom technologies (e.g RFID) have the potential to support low-cost battery-free tag tracking, cost complexity of commissioning a space with beacons readers has stifled adoption. In this paper, we explore how WiFi backscatter can be realized using existing infrastructure already deployed data applications. We present new approach that leverages enable extremely...

Should you decide to launch a nano-satellite today in Low-Earth Orbit (LEO), the cost of renting ground station communication infrastructure is likely significantly exceed your costs. While space costs have lowered with innovative vehicles, private players, and smaller payloads, access remains luxury. This especially true for LEO satellites that are only visible at any location few tens minutes day whose signals extremely weak, necessitating bulky expensive infrastructure.

If you were to launch a small satellite in Low-Earth Orbit (LEO) today, the cost of installing/renting ground stations around world maintain global coverage is likely rival costs. While costs for players have reduced significantly due emergence ride sharing programs operated by both public and private organizations, access station infrastructure still luxury immense form factor components involved, which need be installed large, elevated area free from any obstructions.

Rendezvous with sperm whales for biological observations is made challenging by their prolonged dive patterns. Here, we propose an algorithmic framework that codevelops multiagent reinforcement learning-based routing (autonomy module) and synthetic aperture radar-based very high frequency (VHF) signal-based bearing estimation (sensing maximizing rendezvous opportunities of autonomous robots whales. The sensing module compatible low-energy VHF tags commonly used tracking wildlife. autonomy...

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 integrity and potentially leading to catastrophic damage collapse. Traditionally, displacements large structures are measured using visual sensing platforms advanced surveying equipment. However, they either fall short varying weather lighting conditions require installation...

Tire wear is a leading cause of accidents. measured either manually, or by embedding sensors in tires, using off-tire sensors. Manual sensing extremely tedious. Sensors embedded tire treads are challenging to design and expensive embed. Off-tire like laser range finders prone debris that may settle grooves. To overcome these shortcomings, we propose mmWave radar based sensor, which easy install, continuously provides accurate robust measurements even the presence debris.

Robotic geo-fencing and surveillance systems require accurate monitoring of objects if/when they violate perimeter restrictions. In this paper, we seek a solution for depth imaging such interest at high accuracy (few tens cm) over extended ranges (up to 300 meters) from single vantage point, as pole mounted platform. Unfortunately, the rich literature in using camera, lidar radar isolation struggles meet these tight requirements real-world conditions. This paper proposes Metamoran, that...

Simulation is an invaluable tool for radio-frequency system designers that enables rapid prototyping of various algorithms imaging, target detection, classification, and tracking. However, simulating realistic radar scans a challenging task requires accurate model the scene, radio frequency material properties, corresponding synthesis function. Rather than specifying these models explicitly, we propose DART - Doppler Aided Radar Tomography, Neural Radiance Field-inspired method which uses...

In this paper, we ask, "Can millimeter-wave (mmWave) radars sense objects not directly illuminated by the radar - for instance, located outside transmit beamwidth, behind occlusions, or placed fully radar?" Traditionally, mmWave are limited to that and scatter its signals back. practice, however, other intermediate in environment undergo multiple bounces before being received back at radar. present Hydra, a framework explicitly model exploit multi-bounce paths sensing. Hydra enables...

Millimeter wave radars can perceive through occlusions like dust, fog, smoke and clothes. But compared to cameras lidars, their perception quality is orders of magnitude poorer. RadarHD [3] tackles this problem poor by creating a machine learning super resolution pipeline trained against high lidar scans mimic lidar. ingests low radar generates lidar-like point clouds even in occluded settings. also make use the output for typical robotics tasks odometry, mapping classification using...

This paper explores a machine learning approach for generating high resolution point clouds from single-chip mmWave radar. Unlike lidar and vision-based systems, radar can operate in harsh environments see through occlusions like smoke, fog, dust. Unfortunately, current processing techniques offer poor spatial compared to clouds. presents RadarHD, an end-to-end neural network that constructs lidar-like low input. Enhancing images is challenging due the presence of specular spurious...

The advent of machine learning has resulted in the rapid development accelerators that are capable computing tensor operations efficiently. Specifically, these compute matrix-matrix multiplication, a key routine linear algebra libraries and learning. While using would result high performance radar signal processing, algorithms used often require significant redesign order to efficiently map them on existing machine-learning hardware. In this paper, we show higher levels abstraction...

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We demonstrate Platypus, a sub-millimeter micro-displacement sensing system presented in [3]. Micro-displacement measurement is crucial task industrial systems such as structural health monitoring, where millimeter-level displacement of specific points on the structure or machinery parts can jeopardize integrity and potentially leading to catastrophic damage collapse. Platypus enables level accuracy by using mmWave backscatter tags their reflection phase carriers shift changes due tiny...

In this paper, we demonstrate Osprey, a tire wear sensor presented in [4]. Osprey makes use of commodity automotive, mmWave RADAR, places it the well automobiles to image and then measures wear. accurate continuously while being resilient road debris without embedding any electronics tires. achieves by building super resolution algorithm based on Inverse Synthetic Aperture RADAR imaging thin metallic strips along coded patterns grooves combat debris. Here, implement rotation rig ability...

mmWave radars offer excellent depth resolution even at very long ranges owing to their high bandwidth. But angular is least an order-of-magnitude worse than camera and lidar systems. Hence, radar not a capable 3-D imaging solution in isolation. We propose Metamoran, system that combines the complimentary strengths of obtain accurate, images over clutter environments, all from single fixed vantage point. Metamoran enables rich long-range with applications security surveillance, roadside...

In this paper, we demonstrate Adaptive Millimetro as an extension of Millimetro, ultra-low power millimeter-wave (mmWave) retro-reflector presented in [1], for high mobility scenarios. makes use automotive radars and enables communication with accurate localization roadside infrastructure overextended distances (i.e. >100m). achieves by designing ultra-low-power retro-reflective tags that operate the mmWave frequency band can be embedded road signs, pavements, bi-cycles, or even clothing...