\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[english]{babel}



\usepackage{float}








\begin{document}

\title{An Optimization Method Combining RSSI and PDR Data to Estimate Distance
Between Smart Devices}



\author[1]{Bo Zhao }%
\author[2]{Chao Zheng }%
\author[2]{Xinxin Ren }%
\author[2]{Jianrong Dai }%
\affil[1]{Department of Engineering Physics}%
\affil[2]{Affiliation not available}%


\vspace{-1em}



  
  \date{June 12, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
Distance estimation methods arise in many applications, such as indoor
positioning and Covid-19 contact tracing. The received signal strength
indicator (RSSI) is favored in distance estimation. However, the
accuracy is not satisfactory due to the signal fluctuation. Besides, the
RSSI-only method has a large ranging error because it uses fixed
parameters of the path loss model. Here, we propose an optimization
method combining RSSI and pedestrian dead reckoning (PDR) data to
estimate the distance between smart devices. The PDR may provide the
high accuracy of walking distance and direction, which is used to
compensate for the effects of interference on the RSSI. Moreover, the
parameters of the path loss model are optimized to dynamically fit to
the complex electromagnetic environment. The proposed method is
evaluated in outdoor and indoor environments and is also compared with
the RSSI-only method. The results show that the mean absolute error is
reduced up to 0.51 m and 1.02 m, with the improvement of 10.60\% and
64.55\% for outdoor and indoor environments, respectively, in comparison
with the RSSI-only method. Consequently, the proposed optimization
method has better accuracy of distance estimation than the RSSI-only
method, and its feasibility is demonstrated through real-world
evaluations.%
\end{abstract}%



\sloppy




\selectlanguage{english}
\FloatBarrier
\end{document}

