GNSS Spoofing Network Monitoring Based on Differential Pseudorange

Abstract

Spoofing is becoming a serious threat to various Global Navigation Satellite System (GNSS) applications, especially for those that require high reliability and security such as power grid synchronization and applications related to first responders and aviation safety. Most current works on anti-spoofing focus on spoofing detection from the individual receiver side, which identifies spoofing when it is under an attack. This paper proposes a novel spoofing network monitoring (SNM) mechanism aiming to reveal the presence of spoofing within an area. Consisting of several receivers and one central processing component, it keeps detecting spoofing even when the network is not attacked. The mechanism is based on the different time difference of arrival (TDOA) properties between spoofing and authentic signals. Normally, TDOAs of spoofing signals from a common spoofer are identical while those of authentic signals from diverse directions are dispersed. The TDOA is measured as the differential pseudorange to carrier frequency ratio (DPF). In a spoofing case, the DPFs include those of both authentic and spoofing signals, among which the DPFs of authentic are dispersed while those of spoofing are almost overlapped. An algorithm is proposed to search for the DPFs that are within a pre-defined small range, and an alarm will be raised if several DPFs are found within such range. The proposed SNM methodology is validated by simulations and a partial field trial. Results show 99.99% detection and 0.01% false alarm probabilities are achieved. The SNM has the potential to be adopted in various applications such as (1) alerting dedicated users when spoofing is occurring, which could significantly shorten the receiver side spoofing cost; (2) in combination with GNSS performance monitoring systems, such as the Continuous Operating Reference System (CORS) and GNSS Availability, Accuracy, Reliability anD Integrity Assessment for Timing and Navigation (GAARDIAN) System, to provide more reliable monitoring services.

Statistical test for GNSS spoofing attack detection by using multiple receivers on a rigid body

“…• Using multiple GNSS receivers to detect the presence of a spoofer, exploiting the fact that all GNSS receivers show the same position while being spoofed [37][38][39][40][41][42]. • Range measurements may be used directly to detect the spoofing attack [43][44][45][46][47]. More specifically, [43] considers multiple vehicles where each of them is equipped with a GNSS receiver and a range measurement sensor.…”

Statistical analysis of spoofing detection based on TDOA

“…Such a setup for the spoofed case has been adopted in Ref. for anti‐spoofing performance validation purposes.…”

“…Refs focus on the spoofing simulation techniques while Refs mainly dealing with spoofing detection. The contributions made toward the detection of spoofing can be categorized into four classes, namely, encrypted signal based, cryptographic based, correlation peak anomaly monitoring and signal consistency monitoring . The first class described in Refs is effective, but it requires a high bandwidth network connection between the trusted and user receivers to transfer a great number of intermediate frequency (IF) GNSS signals continuously.…”

“…However, it may fail if there is no relative movement between the spoofer and user receiver (e.g., (i) the spoofer and the receiver(s) are fixed at the common platform; (ii) both the receiver and the spoofer are static). Refs monitor the signal‐in‐space geometry characterization of the received signals. Spoofing signals' geometries are identical as they are from the same direction, while true signals from different directions are not.…”

“…Ref. proposed a spoofing monitoring mechanism that aims to reveal the presence of spoofing within an area. It can be effective in detecting and classifying spoofing signals; however, it requires a long baseline (e.g., several hundred meters) to achieve a good anti‐spoofing performance.…”