Evaluation of RF Fingerprinting-Aided RSS-Based Target Localization for Emergency Response

Abstract

Target localization is essential for emergency dispatching situations. Maximum likelihood estimation (MLE) methods are widely used to estimate the target position based on the received signal strength measurements. However, the performance of MLE solvers is significantly affected by the initialization (i.e., initial guess of the solution or solution search space). To address this, a previous study proposed the semidefinite programming (SDP)-based MLE initialization. However, the performance of the SDP-based initialization technique is largely affected by the shadowing variance and geometric diversity between the target and receivers. In this study, a radio frequency (RF) fingerprinting-based MLE initialization is proposed. Further, a maximum likelihood problem for target localization combining RF fingerprinting is formulated. In the three test environments of open space, urban, and indoor, the proposed RF fingerprinting-aided target localization method showed a performance improvement of up to 63.31% and an average of 39.13%, compared to the MLE algorithm initialized with SDP. Furthermore, unlike the SDP-MLE method, the proposed method was not significantly affected by the poor geometry between the target and receivers in our experiments.

Submitted to the 2022 IEEE 95th Vehicular Technology Conference (VTC2022-Spring)

References in corpus (5)

• Optimal Parameter Inflation to Enhance the Availability of Single-Frequency GBAS for Intelligent Air Transportation
• Enhanced Accuracy Simulator for a Future Korean Nationwide eLoran System
• SFOL DME Pulse Shaping Through Digital Predistortion for High-Accuracy DME
• Single-Antenna-Based GPS Antijamming Method Exploiting Polarization Diversity
• First Demonstration of the Korean eLoran Accuracy in a Narrow Waterway Using Improved ASF Maps

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