Evaluation of a Signal Anomaly Classification Method for GNSS Spoofing Detection by Using GNSS Observation Data

This paper proposes a real-time GNSS spoofing detection method using a simplified signal anomaly classification approach. It utilizes readily available parameters from observation data, focusing on signal strength and satellite elevation information. The method employs statistical measures and the IQR method to evaluate signal characteristics, establishing a classification flow from "Definite Spoofing" to "Good Situation". Spoofing experiments, including non-coherent and coherent attacks, were conducted to evaluate the method's performance. This approach aims to provide a simple, effective, and widely implementable method for enhancing GNSS security across various applications.