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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.04813 |
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Table of Contents:
- Delay-Doppler Map (DDM) noise-floor observations from the Cyclone Global Navigation Satellite System (CYGNSS) constellation provide a practical means for spaceborne detection of GNSS radio frequency interference (RFI). Existing CYGNSS analyses use NASA's kurtosis-based flag product or mean aggregation of the four simultaneous DDM noise-floor values at each epoch. However, these DDMs are formed from different reflected GNSS signals received through two nadir antennas with different orientations. Thus, ground-based RFI may raise only some channel noise floors, depending on antenna gain and viewing geometry. Mean aggregation can dilute the strongest anomaly with unaffected channels, causing missed detections. This paper replaces the mean with the maximum of four co-temporal DDM noise-floor values. This statistic preserves channel-level anomalies and accounts for channel-dependent exposure. A practical 41 dB threshold is established using low-RFI reference regions and documented or persistent interference environments, enabling simple detection without image-level classification or raw intermediate-frequency processing. To reduce isolated false alarms, a verification stage uses multi-satellite concurrence and temporal persistence over a 10 s window. The method is evaluated using CYGNSS Level 1 data from May 2025 over the White Sands Missile Range, where NOTAM-announced GPS jamming tests provide documented interference conditions, and the Middle East, where persistent RFI has been reported. In the White Sands case, the proposed method detected RFI on three dates where the mean-based method produced negligible detections. In the Middle East, it flagged 62% of observed epochs, compared with 46% for the mean-based method and 33% for the kurtosis-based method. These results show that maximum-based aggregation offers a simple, lightweight improvement over existing CYGNSS DDM noise-floor methods.