Search for: All records

We present the design, implementation, and evaluation of MiFly, a self-localization system for autonomous drones that works across indoor and outdoor environments, including low-visibility, dark, and GPS-denied settings. MiFly performs 6DoF self-localization by leveraging a single millimeter-wave (mmWave) anchor in its vicinity- even if that anchor is visually occluded. MiFly’s core contribution is in its joint design of a mmWave anchor and localization algorithm. The lowpower anchor features a novel dual-polarization dual-modulation architecture, which enables single-shot 3D localization. MmWave radars mounted on the drone perform 3D localization relative to the anchor and fuse this data with the drone’s internal inertial measurement unit (IMU) to estimate its 6DoF trajectory. We implemented and evaluated MiFly on a DJI drone. We collected over 6,600 localization estimates across different trajectory patterns and demonstrate a median localization error of 7 cm and a 90th percentile less than 15 cm, even in low-light conditions and when the anchor is fully occluded (visually) from the drone. Demo video: youtu.be/LfXfZ26tEok 

This paper investigates how an airborne node can eavesdrop on the underwater acoustic communication between submerged nodes. Conventionally, such eavesdropping has been assumed impossible as acoustic signals do not cross the water-air boundary. Here, we demonstrate that underwater acoustic communications signals can be picked up and (under certain conditions) decoded using an airborne mmWave radar due to the minute vibrations induced by the communication signals on the water surface. We implemented and evaluated a proof-of-concept prototype of our method and tested it in controlled (pool) and uncontrolled environments (lake). Our results demonstrate that an airborne device can identify the modulation and bitrate of acoustic transmissions from an uncooperative underwater transmitter (victim), and even decode the transmitted symbols. Unlike conventional over-the-air communications, our results indicate that the secrecy of underwater links varies depending on the modulation type and provide insights into the underlying reasons behind these differences. We also highlight the theoretical limitations of such a threat model, and how these results may have a significant impact on the stealthiness of underwater communications, with particular concern to submarine warfare, underwater operations (e.g., oil & gas, search & rescue, mining), and conservation of endangered species. Finally, our investigation uncovers countermeasures that can be used to improve or restore the stealthiness of underwater acoustic communications against such threats. 

We present RL2, a robotic system for efficient and accurate localization of UHF RFID tags. In contrast to past robotic RFID localization systems, which have mostly focused on location accuracy, RL2 learns how to jointly optimize the accuracy and speed of localization. To do so, it introduces a reinforcement learning-based (RL) trajectory optimization network that learns the next best trajectory for a robot-mounted reader antenna. Our algorithm encodes the aperture length and location confidence (using a synthetic-aperture-radar formulation) from multiple RFID tags into the state observations and uses them to learn the optimal trajectory. We built an end-to-end prototype of RL2 with an antenna moving on a ceiling-mounted 2D robotic track. We evaluated RL2 and demonstrated that with the median 3D localization accuracy of 0.55m, it locates multiple RFID tags 2.13x faster compared to a baseline strategy. Our results show the potential for RL-based RFID localization to enhance the efficiency of RFID inventory processes in areas spanning manufacturing, retail, and logistics.