CORE Metadata, citation and similar papers at core.ac.uk Provided by Institutional Knowledge at Singapore Management University Singapore Management University Institutional Knowledge at Singapore Management University Research Collection School Of Information Systems School of Information Systems 11-2017 Enabling phased array signal processing for mobile WiFi devices Kun QIAN Chenshu WU Zheng YANG Zimu ZHOU Singapore Management University, [email protected] Xu WANG See next page for additional authors Follow this and additional works at: https://ink.library.smu.edu.sg/sis_research Part of the Digital Communications and Networking Commons, and the Software Engineering Commons Citation QIAN, Kun; WU, Chenshu; YANG, Zheng; ZHOU, Zimu; WANG, Xu; and LIU, Yunhao. Enabling phased array signal processing for mobile WiFi devices. (2017). IEEE Transactions on Mobile Computing. 17, (8), 1820-1833. Research Collection School Of Information Systems. Available at: https://ink.library.smu.edu.sg/sis_research/4879 This Journal Article is brought to you for free and open access by the School of Information Systems at Institutional Knowledge at Singapore Management University. It has been accepted for inclusion in Research Collection School Of Information Systems by an authorized administrator of Institutional Knowledge at Singapore Management University. For more information, please email [email protected]. Author Kun QIAN, Chenshu WU, Zheng YANG, Zimu ZHOU, Xu WANG, and Yunhao LIU This journal article is available at Institutional Knowledge at Singapore Management University: https://ink.library.smu.edu.sg/sis_research/4879 1820 IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 17, NO. 8, AUGUST 2018 Enabling Phased Array Signal Processing for Mobile WiFi Devices Kun Qian , Student Member, IEEE, Chenshu Wu , Member, IEEE, Zheng Yang , Member, IEEE, Zimu Zhou, Member, IEEE, Xu Wang, Student Member, IEEE, and Yunhao Liu, Fellow, IEEE Abstract—Modern mobile devices are equipped with multiple antennas, which brings various wireless sensing applications such as accurate localization, contactless human detection, and wireless human-device interaction. A key enabler for these applications is phased array signal processing, especially Angle of Arrival (AoA) estimation. However, accurate AoA estimation on commodity devices is non-trivial due to limited number of antennas and uncertain phase offsets. Previous works either rely on elaborate calibration or involve contrived human interactions. In this paper, we aim to enable practical AoA measurements on commodity off-the-shelf (COTS) mobile devices. The key insight is to involve users’ natural rotation to formulate a virtual spatial-temporal antenna array and conduce a relative incident signal of measurements at two orientations. Then by taking the differential phase, it is feasible to remove the phase offsets and derive the accurate AoA of the equivalent incoming signal, while the rotation angle can also be captured by built-in inertial sensors. On this basis, we propose Differential MUSIC (D-MUSIC), a relative form of the standard MUSIC algorithm that eliminates the unknown phase offsets and achieves accurate AoA estimation on COTS mobile devices with only one rotation. We further extend D- MUSIC to 3-D space, integrate extra measurements during rotations for higher estimation accuracy, and fortify it in multipath-rich scenarios. We prototype D-MUSIC on commodity WiFi infrastructure and evaluate it in typical indoor environments. Experimental results demonstrate a superior performance with average AoA estimation errors of 13 with only three measurements and 5 with at most 10 measurements. Requiring no modifications or calibration, D-MUSIC is envisioned as a promising scheme for practical AoA estimation on COTS mobile devices. Index Terms—Channel state information, angle of arrival, phased array calibration Ç 1INTRODUCTION ECENT years have witnessed the conceptualization and Despite the potential for a myriad of wireless sensing Rdevelopment of wireless sensing, especially using multi- applications, accurate AoA measurement is non-trivial on antenna devices. Various innovative systems are designed to commodity devices. In principle, it is possible to obtain the localize and track mobile devices accurately [1], detect and incident signals’ directions with a large antenna array. Yet pinpoint human movements contactlessly [2], and enable most commercial mobile devices are installed with limited human-device interaction wirelessly [3], [4]. A key to such number of antennas (typically fewer than three), making it applications is to enable phased array signal processing, infeasible to directly derive precise AoA measurements. which makes various comparisons of signals received from Even worse, the uncertain phase offsets on commodity WiFi each of the antennas of commodity devices. Particularly, devices can dramatically deteriorate the performance of deriving spatial direction of incoming wireless signals, i.e., classical AoA estimation algorithms e.g. MUltiple SIgnal the Angle of Arrival (AoA), serves as the basis for a number Classification (MUSIC) [9], leading to unacceptable AoA of applications including accurate indoor localization [5], estimation errors. Pioneer works that achieve accurate AoA secure wireless communication [6], wireless coverage confin- measurements either work only for fixed devices with ing [7] and spatial-aware device interaction [8]. known relative locations between transceivers [10], or involves contrived human intervention to emulate an antenna array to perform sophisticated Synthetic Aperture K. Qian, Z. Yang, X. Wang, and Y. Liu are with the School of Software Radar (SAR) [11]. The vision of AoA estimation on Com- and Tsinghua National Laboratory for Information Science and Technol- mercial Off-The-Shelf (COTS) mobile devices without extra ogy, Tsinghua University, Beijing 100084, China. efforts entails great challenges and still remains open. E-mail: {qiank10, hmilyyz, darenwang11, yunhaoliu}@gmail.com. C. Wu is with the School of Software and Tsinghua National Laboratory In this paper, we ask the question: can we achieve accurate for Information Science and Technology, Tsinghua University, Beijing AoA measurements on COTS mobile devices without modification 100084, China and the Jiangsu High Technology Research Key Laboratory and with minimal human interaction? As illustrated in Fig. 1, for Wireless Sensor Networks, Jiangsu 210024, China. E-mail: [email protected]. the key insight is to involve rotation, a natural and angle- Z. Zhou is with the Computer Engineering and Networks Laboratory, aware user motion, to formulate a virtual spatial-temporal ETH Zurich, Zurich 8092, Switzerland. E-mail: [email protected]. antenna array and a relative incident wireless signal. Specifi- Manuscript received 29 May 2016; revised 6 Nov. 2017; accepted 19 Nov. cally, conventional AoA estimation schemes either formulate 2017. Date of publication 28 Nov. 2017; date of current version 29 June 2018. a spatial array (via physical antennas) or temporal array (via (Corresponding author: Kun Qian.) SAR). Yet we take the difference between measurements of For information on obtaining reprints of this article, please send e-mail to: [email protected], and reference the Digital Object Identifier below. one antenna array at two orientations and transform two Digital Object Identifier no. 10.1109/TMC.2017.2778155 incident signals into an equivalent relative incident signal. 1536-1233 ß 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html_ for more information. QIAN ET AL.: ENABLING PHASED ARRAY SIGNAL PROCESSING FOR MOBILE WIFI DEVICES 1821 derives AoA with an average estimation error of 13,while standard MUSIC totally fails to yield correct AoA estimation. Particularly, comparable AoA accuracy also holds in 3-D space. We also integrate D-MUSIC as an auto phase correction for previous calibration-based schemes, which yields similar accuracy compared to those obtained by precise manual cali- bration. Since D-MUSIC achieves delightful performances with neither hardware modifications nor contrived user inter- vention, we envision it as a promising step towards practical AoA estimation on commodity mobile WiFi receivers, which underpins new insights for plentiful applications in wireless sensing. In summary, the main contributions are as follows: We present a novel differential MUSIC algorithm that enables AoA estimation on COTS mobile devices by formulating a virtual spatial-temporal antenna array. Fig. 1. An illustrative example of D-MUSIC. D-MUSIC operates with only natural and easy user actions, requiring no hardware modifications, cum- Such a spatial-temporal formulation enjoys two advantages: bersome calibration, or contrived human intervention. (1) Since the intrinsic phase offset is unknown yet constant We extend the applicability of D-MUSIC to 3-D cases for each individual antenna, taking the differential phase on with diverse transmitter and receiver heights, which two measurements naturally remove the phase offset since exceeds the achievements of previous schemes. We the antennas are identical. (2) Since the phase measurements further integrate multiple measurements from multi- of the equivalent incident signal are free of phase offset, the ple orientations to form a larger spatial-temporal equivalent incident angle can be easily derived using stan- antenna array for higher