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WAWA: Wavelet Analysis-Based Watermarking Authentication for GNSS Civil Signal with Immediate Symbol-Level
Xinyu Tang1,2, Xiaomei Tang1,2, Honglei Lin1,2
1College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China.
This study introduces WAWA, a Wavelet Analysis-based Watermarking Authentication scheme for Global Navigation Satellite System (GNSS) signals. WAWA enables immediate, physical-layer authentication, overcoming latency and memory issues in current systems.
Area of Science:
- Signal Processing
- Cryptography
- Navigation Systems
Background:
- Existing Global Navigation Satellite System (GNSS) authentication methods face challenges with high verification latency and memory demands.
- Time-sensitive applications are vulnerable to spoofing due to limitations in transmitting cryptographic credentials over bandwidth-constrained GNSS signals.
Purpose of the Study:
- To introduce a novel physical-layer authentication scheme for GNSS signals that addresses current limitations.
- To enable immediate, symbol-level authentication without synchronization or large memory buffers.
Main Methods:
- Developed WAWA (Wavelet Analysis-based Watermarking Authentication) operating at the GNSS physical layer.
- Utilized the wavelet domain for high-capacity data embedding of public-key digital signatures.
- Designed a dual-path verification framework for standalone and network-assisted receivers.
Main Results:
- WAWA achieves immediate, symbol-level authentication, eliminating verification delay.
- The scheme offers superior effective bandwidth and low memory overhead, suitable for resource-constrained devices.
- Introduced a controllable signal correlation loss, balanced against security and efficiency.
Conclusions:
- WAWA provides an exceptional balance of security, immediacy, and resource efficiency for GNSS authentication.
- Offers a promising solution for trustworthy Positioning, Navigation, and Timing (PNT) sensor data in critical applications.
- Enables robust authentication for large-scale sensor networks and autonomous systems.
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