Abstract
Indoor emitter localization is a topic of continued interest for improving wireless security as wireless technologies continue to become more advanced. Conventional methods have focused on the localization of devices relative to multi-sensor systems owing to ease of implementation with pre-existing infrastructures. This work, however, focuses on enhancing wireless security via non-cooperative emitter localization in scenarios where only a single receiver can be employed. A vector sensor is simulated and experimentally developed that extracts three-dimensional signal characteristics for room-based emitter localization and is compared to conventional methodologies such as Received Signal Strength (RSS), Time of Arrival (ToA), and Direction of Arrival (DoA). The proposed method generates time-frequency fingerprints and extracts features through dimensionality reduction. A second stage extracts spatial parameters consisting of Channel State Information (CSI) and DoAs that are analyzed using a Gaussian Mixture Model (GMM) to segregate fine-grained regions of interest within each room where the non-cooperative emitter resides. Blind channel equalization cascaded with a least squares channel estimate is used for acquiring the CSI, whereas the DoAs are obtained by unique trigonometric properties of the vector sensing antenna. The results demonstrate that a vector sensor can improve non-cooperative emitter localization and enhance wireless security in indoor environments.
Highlights
The Global Positioning System (GPS) is extraordinarily effective in outdoor applications, GPS is less effective when used as an Indoor Positioning System (IPS) since it encountersNon-Line-of-Sight (NLoS) signal propagation
It is observed that the proposed method achieves 84% and nearly perfect accuracy for STFT and Wavelet Transform (WT) fingerprints, causing a less significant loss of 2% in room localization performance when training on 80% of the dataset and testing on the remaining 20%
The results indicate that the STFT achieves better overall room localization than the WT and Received Signal Strength (RSS) approach for both Global System for Mobile Communication (GSM) and Universal for Mobile Telecommunications System (UMTS) emissions regardless of the polarization
Summary
The Global Positioning System (GPS) is extraordinarily effective in outdoor applications, GPS is less effective when used as an Indoor Positioning System (IPS) since it encounters. Limited investigations have explored non-cooperative emitter localization with single receiver systems by extracting ToA information from a mobile receiver along with time deviations embedded within multipath channels [17,18,19,20] These studies have not been experimentally validated. Localization performance is first evaluated by exploring room-based localization (i.e., detecting the room in which the non-cooperative emitter resides) from a Weighted K-Nearest-Neighbor (WKNN) classifier that analyzes time-frequency features of the proposed approach, compared to conventional features extracted from signals received by a dipole antenna such as RSS, ToA, DoA, and their combinations from a simulation standpoint. The novelty of this work lies in the first implementation of the three-element vector sensor for use with machine learning algorithms and a proposed fingerprinting approach when localizing a non-cooperative device in an indoor environment with a single receiver
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