Multi-Frequency Based CSI Compression for Vehicle Localization in Intelligent Transportation System

With the advent of the new era of 6G, new applications of smart factories and intelligent transportation systems based on real-time wireless sensing technology will confront great demands and challenges. In the intelligent transportation system, it is essential to realize services such as localization and intrusion detection for intelligent vehicles. To build a wide range of positioning network based on large-scale wireless networks, it is of great challenge to simultaneously solve the problem of unacceptable delay and bandwidth requirements caused by a large number of channel state information (CSI) data transmission. Therefore, we propose a novel algorithm, named PAOFIT, where a projection transformation aided CSI curve fitting compression algorithm is firstly proposed to decrease data distortions by improving the orthogonality of signal subspace and noise subspace, and an adaptive weighted average fitting order judgment algorithm is proposed to calculate the fitting order needed in the curve fitting process. Then, localization parameter, time of flight (ToF) are estimated by CSI reconstruction and parameter estimation. Finally, the location of the target is obtained by substituting these parameters into time difference of arrival (TDoA) wireless localization technology. Extensive experimental results verify that, compared with the existing compression algorithms, the proposed PAOFIT has a better performance in terms of compression ratio, median positioning error, residual and execution time.