Abstract:Global Navigation Satellite System (GNSS) interference increasingly threatens positioning, navigation, and timing services and requires monitoring over areas beyond dense ground networks. Low Earth orbit satellites provide complementary regional coverage, and previous studies have shown that terrestrial radio-frequency interference (RFI) produces measurable anomalies in spaceborne GNSS and GNSS-reflectometry observations. The Cyclone Global Navigation Satellite System (CYGNSS) has been used to map and characterize interference, but existing approaches generally depend on long-term data accumulation, full Delay-Doppler Map (DDM) processing, or special raw intermediate-frequency acquisitions and lack epoch-level validation against independent data. This paper presents a lightweight detector using four channel-wise DDM noise-floor values routinely distributed in CYGNSS Level-1 products. Because the channels observe different reflected signals through two nadir antennas, interference-related elevations may be strongly asymmetric. The detector uses the channel maximum to preserve these responses and applies temporal-persistence or multi-satellite-concurrence screening to reject isolated anomalies. Evaluation at the White Sands Missile Range used an independent reference formed from Federal Aviation Administration Notices to Air Missions and concurrent Automatic Dependent Surveillance-Broadcast navigation-integrity degradation. Relative to the single-epoch mean baseline, the proposed detector increased the probability of detection by 0.063 at the same observed false-alarm rate, with a 95% confidence interval of [0.034, 0.094]. In the Middle East, it also detected both vertical-stripe and atypical elevated-background DDM structures. These results demonstrate lightweight epoch-level monitoring of wide-area or temporally sustained GNSS interference using routine Level-1 products.
From: Ji-Hyeon Shin [view email]
[v1]
Thu, 5 Mar 2026 04:58:48 UTC (1,177 KB)
[v2]
Mon, 9 Mar 2026 09:38:18 UTC (1,177 KB)
[v3]
Thu, 30 Apr 2026 05:28:22 UTC (949 KB)
[v4]
Tue, 28 Jul 2026 12:36:39 UTC (985 KB)
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