CSI技术在室内定位中的应用与FILA方法

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"基于CSI的室内本地化" 在无线通信领域，室内定位系统已经变得越来越重要，因为它们可以为室内环境中的位置服务提供支持。尤其在WiFi技术普及的今天，基于WiFi的室内定位因其开放访问和低成本特性而备受青睐。然而，传统上依赖接收信号强度指示器（RSSI）的距离估计方法常常受到多径效应导致的时空变化影响，这极大地增加了当前系统中的定位误差。针对这一问题，本文深入到物理层进行分析，并考虑了不理想的RSSI读数对定位精度的影响。研究者们探索了正交频分复用（OFDM）系统中子载波的频率多样性，并提出了一种新颖的方法，称为FILA（利用CSI的指纹定位算法）。FILA方法的核心是利用信道状态信息（CSI）来构建传播模型和接收端的指纹识别系统。 FILA系统是在商业802.11网络接口卡（NIC）上实现的，这意味着它可以与现有的WiFi硬件兼容。通过实验证明，这种方法能够提高室内定位的准确性，降低了由多径传播引起的误差。实验结果展示了FILA系统在实际部署中的优秀性能，为WiFi基的室内定位提供了更稳定、更精确的解决方案。此外，文章还可能详细讨论了以下方面： 1. CSI获取：如何从802.11 NICs获取详细的CSI数据，包括每个子载波的幅度和相位信息，以及这些信息如何反映无线信号在室内环境中的传播特性。 2. 模型建立：FILA如何利用这些 CSI 数据来构建室内环境的无线传播模型，该模型可能包括反射、折射和散射等多径效应的建模。 3. 指纹库构建：描述了如何收集和处理大量的CSI指纹，创建一个室内空间的“指纹地图”，以便后续的定位过程中进行匹配。 4. 定位算法：详细解释了FILA的定位算法，包括匹配过程、误差校正和定位估计策略。 5. 实验设置和评估：可能包括不同的室内环境、不同距离和障碍物条件下的测试，以及与基于RSSI的传统方法的性能对比。 6. 应用场景和未来工作：可能讨论了FILA在室内导航、安全监控、物联网应用等领域的潜力，以及未来可能的研究方向，如提高实时性、增强鲁棒性或适应更多变的环境条件。这篇研究论文探讨了一种创新的基于CSI的室内定位方法，通过克服RSSI的不稳定性，提升了WiFi室内定位的精度，为相关领域的研究提供了新的思路和技术手段。

CSI-Based Indoor Localization

Kaishun Wu, Member, IEEE, Jiang Xiao, Student Member, IEEE,

Youwen Yi, Student Member, IEEE, Dihu Chen, Member, IEEE,

Xiaonan Luo, Member, IEEE, and Lionel M. Ni, Fellow, IEEE

Abstract—Indoor positioning systems have received increasing attention for supporting location-based services in indoor

environments. WiFi-based indoor localization has been attractive due to its open access and low cost properties. However, the

distance estimation based on received signal strength indicator (RSSI) is easily affected by the temporal and spatial variance due to

the multipath effect, which contributes to most of the estimation errors in current systems. In this work, we analyze this effect across

the physical layer and account for the undesirable RSSI readings being reported. We explore the frequency diversity of the subcarriers

in orthogonal frequency division multiplexing systems and propose a novel approach called FILA, which leverages the channel state

information (CSI) to build a propagation model and a fingerprinting system at the receiver. We implement the FILA system on

commercial 802.11 NICs, and then evaluate its performance in different typical indoor scenarios. The experimental results show that

the accuracy and latency of distance calculation can be significantly enhanced by using CSI. Moreover, FILA can significantly improve

the localization accuracy compared with the corresponding RSSI approach.

Index Terms—Indoor localization, channel state information, RSSI, physical layer

1INTRODUCTION

OCALIZATION is one of the essential modules of many

mobile wireless applications. Although global position-

ing system (GPS) works extremely well for an open-air

localization, it does not perform effectively in indoor

environments du e to the disabilit y of GPS signals to

penetrate in-building materials. Therefore, precise indoor

localization is still a critical missing component and has

been gaining growing interest from a wide range of

applications, e.g., location detection of assets in a ware-

house, patient tracking inside the building of the hospital,

and emergency personnel positioning in a disaster area.

A great number of researches have been done to address

the indoor localization problem. Many range-based locali-

zation protocols compute positions based on received

signal strength indicator (RSSI), which represents the

received power level at the receiver. According to propaga-

tion loss model [1], received signal power monotonically

decreases with increasing distance from the source, which

is the foundation of the model-based localization. Most of

the existing radio frequency (RF)-based indoor localization

are based on the RSSI values [1], [2], [3], [4], [5]. More

related work is in the supplemental file, which can be found

on the Computer Society Digital Library at http://

doi.ieeecomputersociety.org/10.1109/TPDS.2012.214. How-

ever, we claim that the fundamental reasons why RSSI is

not suitable for indoor localization are from two aspects:

First, RSSI is measured from the RF signal at a per packet

level, which is difficult to obtain an accurate value.

According to our measurement in a typical indoor environ-

ment as shown in Fig. 1, the variance of RSSIs collected

from an immobile receiver in 1 minute is up to 5 dB.

Second, RSSI is easily varied by the multipath effect. In

theory, it is possible to establish a model to estimate the

separation distance using the received power. In reality,

however, the propagation of a RF wave is attenuated by

reflection when it hits the surface of an obstacle. In addition

to the line-of-sight (LOS) signal, there are possibly multiple

signals arriving at the receiver through different paths. This

multipath effect is even more severe in indoor environ-

ments where a ceiling, floor, and walls are present. As a

result, it is possible for a closer receiver to have a lower

RSSI than a more distant one. Consequently, a simple

relationship between received power and separating dis-

tance cannot be established. Therefore, this time varying

and vulnerable RSSI value creates undesirable localization

errors.

We argue that a reliable metric provided by commercial

NICs to improve the accuracy of indoor localization is in

need. Such metric should be more temporal stable and

provide the capability to benefit from the multipath effect.

In current widely used orthogonal frequency division

multiplexing (OFDM) systems, where data are modulated

on multiple subcarriers in different frequencies and

transmitted simultaneously, we have a value that estimates

the channel in each subcarrier called channel state informa-

tion (CSI). Different from RSSI, CSI is a fine-grained value

from the PHY layer which describes the amplitude and

phase on each subcarrier in the frequency domain. In

contrast to having only one RSSI per packet, we can obtain

1300 IEEE TRANSACTION S ON PARALLEL AND DISTRIBUTED SYSTEMS, VOL. 24, NO. 7, JULY 2013

. K. Wu is with the National Engineering Research Center of Digital Life,

State-Province Joint Laboratory of Digital Home Interactive Applications,

School of Information Science and Technology, Sun Yat-sen University,

Guangzhou 510006, China, and Guangzhou HKUST Fok Ying Tung

Research Institute. E-mail: kwinson@ust.hk.

. J. Xiao, Y. Yi, and L.M. Ni are with the Department of Computer Science

and Engineering, Hong Kong University of Science and Technology, Hong

Kong. E-mail: {jxiao, ywyi, ni}@cse.ust.hk.

. D. Chen is with the School of Physics and Engineering, Sun Yat-sen

University, Guangzhou, China. E-mail: stscdh@mail.sysu.edu.cn.

. X. Luo is with the National Engineering Research Center of Digital Life,

State-Province Joint Laboratory of Digital Home Interactive Applications,

Sun Yat-sen University, Guangzhou, China.

E-mail: lnslxn@mail.sysu.edu.cn.

Manuscript received 27 Dec. 2011; revised 19 June 2012; accepted 2 July 2012;

published online 11 July 2012.

Recommended for acceptance by J. Cao.

For information on obtaining reprints of this article, please send e-mail to:

tpds@computer.org, and reference IEEECS Log Number TPDS-2011-12-0937.

Digital Object Identifier no. 10.1109/TPDS.2012.214.

1045-9219/13/$31.00 ß 2013 IEEE Published by the IEEE Computer Society

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