Indoor NLOS Positioning System Based on Enhanced CSI Feature with Intrusion Adaptability

Abstract

With the wide deployment of commercial WiFi devices, the fine-grained channel state information (CSI) has received widespread attention with broad application domain including indoor localization and intrusion detection. From the perspective of practicality, dynamic intrusion may be confused under non-line-of-sight (NLOS) conditions and the continuous operation of passive positioning system will bring much unnecessary computation. In this paper, we propose an enhanced CSI-based indoor positioning system with pre-intrusion detection suitable for NLOS scenarios (C-InP). It mainly consists of two modules: intrusion detection and positioning estimation. The introduction of detection module is a prerequisite for positioning module. In order to improve the discrimination of features under NLOS conditions, we propose a modified calibration method for phase transformation while the amplitude outliers are filtered by the variance distribution with the median sequence. In addition, binary and improved multiple support vector classification (SVC) models are established to realize NLOS intrusion detection and high-discrimination fingerprint localization, respectively. Comprehensive experimental verification is carried out in typical indoor scenarios. Experimental results show that C-InP outperforms the existing system in NLOS environments, where the mean distance error (MDE) reached 0.49 m in the integrated room and 0.81 m in the complex garage, respectively.

SICD: Novel Single-Access-Point Indoor Localization Based on CSI-MIMO with Dimensionality Reduction

“…For simplicity, we selected CSI amplitude as the data feature. The traditional localization method based on CSI-MIMO requires multiple APs or terminals to construct the MIMO system [ 29 , 30 , 31 ]. In SICD, we use a single AP and terminal with multiple antennas to construct MIMO system.…”

A TDOA and PDR Fusion Method for 5G Indoor Localization Based on Virtual Base Stations in Unknown Areas

“…The localization accuracy of the proposed algorithm is improved by more than 50% on average. K. Han [35] et al established binary and improved multiple support vector classification models to realize NLOS intrusion detection and high-discrimination fingerprint localization, respectively. B. Cao [36] et al employed Gaussian mixed model to re-estimate the measurement distance, and two parallel variational Bayesian adaptive Kalman filters under the structure of interacting multiple models were utilized to smoothen the result of GMM to eliminate the LOS and NLOS errors, respectively.…”