- Publication Date:
- NSF-PAR ID:
- Journal Name:
- 18th USENIX Symposium on Networked Systems Design and Implementation
- Sponsoring Org:
- National Science Foundation
More Like this
The ability for a smart speaker to localize a user based on his/her voice opens the door to many new applications. In this paper, we present a novel system, MAVL, to localize human voice. It consists of three major components: (i) We first develop a novel multi-resolution analysis to estimate the Angle-of-Arrival (AoA) of time-varying low-frequency coherent voice signals coming from multiple propagation paths; (ii) We then automatically estimate the room structure by emitting acoustic signals and developing an improved 3D MUSIC algorithm; (iii) We finally re-trace the paths using the estimated AoA and room structure to localize the voice. We implement a prototype system using a single speaker and a uniform circular microphone array. Our results show that it achieves median errors of 1.49o and 3.33o for the top two AoAs estimation and achieves median localization errors of 0.31m in line-of-sight (LoS) cases and 0.47m in non-line-of-sight (NLoS) cases.
In this paper, we develop the analytical framework for a novel Wireless signal-based Sensing capability for Robotics (WSR) by leveraging a robots’ mobility in 3D space. It allows robots to primarily measure relative direction, or Angle-of-Arrival (AOA), to other robots, while operating in non-line-of-sight unmapped environments and without requiring external infrastructure. We do so by capturing all of the paths that a wireless signal traverses as it travels from a transmitting to a receiving robot in the team, which we term as an AOA profile. The key intuition behind our approach is to enable a robot to emulate antenna arrays as it moves freely in 2D and 3D space. The small differences in the phase of the wireless signals are thus processed with knowledge of robots’ local displacement to obtain the profile, via a method akin to Synthetic Aperture Radar (SAR). The main contribution of this work is the development of (i) a framework to accommodate arbitrary 2D and 3D motion, as well as continuous mobility of both signal transmitting and receiving robots, while computing AOA profiles between them and (ii) a Cramer–Rao Bound analysis, based on antenna array theory, that provides a lower bound on the variance in AOAmore »
In this work, we propose a novel approach for high accuracy user localization by merging tools from both millimeter wave (mmWave) imaging and communications. The key idea of the proposed solution is to leverage mmWave imaging to construct a high-resolution 3D image of the line-of-sight (LOS) and non-line-of-sight (NLOS) objects in the environment at one antenna array. Then, uplink pilot signaling with the user is used to estimate the angle-of-arrival and time-of- arrival of the dominant channel paths. By projecting the AoA and ToA information on the 3D mmWave images of the environment, the proposed solution can locate the user with a sub-centimeter accuracy. This approach has several gains. First, it allows accurate simultaneous localization and mapping (SLAM) from a single standpoint, i.e., using only one antenna array. Second, it does not require any prior knowledge of the surrounding environment. Third, it can locate NLOS users, even if their signals experience more than one reflection and without requiring an antenna array at the user. The approach is evaluated using a hardware setup and its ability to provide sub-centimeter localization accuracy is shown
In this paper we derive a new capability for robots to measure relative direction, or Angle-of-Arrival (AOA), to other robots, while operating in non-line-of-sight and unmapped environments, without requiring external infrastructure. We do so by capturing all of the paths that a WiFi signal traverses as it travels from a transmitting to a receiving robot in the team, which we term as an AOA profile. The key intuition behind our approach is to emulate antenna arrays in the air as a robot moves freely in 2D or 3D space. The small differences in the phase and amplitude of WiFi signals are thus processed with knowledge of a robots’ local displacements (often provided via inertial sensors) to obtain the profile, via a method akin to Synthetic Aperture Radar (SAR). The main contribution of this work is the development of i) a framework to accommodate arbitrary 2D and 3D trajectories, as well as continuous mobility of both transmitting and receiving robots, while computing AOA profiles between them and ii) an accompanying analysis that provides a lower bound on variance of AOA estimation as a function of robot trajectory geometry that is based on the Cramer Rao Bound and antenna array theory. Thismore »
Voice assistants such as Amazon Echo (Alexa) and Google Home use microphone arrays to estimate the angle of arrival (AoA) of the human voice. This paper focuses on adding user localization as a new capability to voice assistants. For any voice command, we desire Alexa to be able to localize the user inside the home. The core challenge is two-fold: (1) accurately estimating the AoAs of multipath echoes without the knowledge of the source signal, and (2) tracing back these AoAs to reverse triangulate the user's location.We develop VoLoc, a system that proposes an iterative align-and-cancel algorithm for improved multipath AoA estimation, followed by an error-minimization technique to estimate the geometry of a nearby wall reflection. The AoAs and geometric parameters of the nearby wall are then fused to reveal the user's location. Under modest assumptions, we report localization accuracy of 0.44 m across different rooms, clutter, and user/microphone locations. VoLoc runs in near real-time but needs to hear around 15 voice commands before becoming operational.