High-Precision Time-of-Arrival Estimation in HF Sensor Networks via Multipath Separation and Independent Tracking.

Area of Science:

• Signal processing
• Radio science
• Sensor networks

Background:

• High-frequency (HF) sensor networks are crucial for remote sensing and emergency communications.
• Ionospheric multipath interference severely degrades Time-of-Arrival (TOA) estimation accuracy in HF systems.
• Conventional methods often misinterpret multipath components as noise, causing bias in dynamic environments.

Purpose of the Study:

• To develop a robust architecture for separating and tracking multipath components in HF signals.
• To enhance the accuracy and reliability of TOA estimation in the presence of ionospheric disturbances.
• To overcome the limitations of traditional methods that treat multipath as noise.

Main Methods:

• Proposed a Multipath Separation and Independent Tracking (MSIT) architecture.
• Utilized a Maximum Likelihood Estimation (MLE) module for iterative signal component separation.
• Employed parallel tracking loops for phase and delay parameter updates.
• Incorporated a super-resolution MUSIC algorithm for initialization to resolve sub-chip multipath components.

Main Results:

• Achieved a mean delay estimation error reduction of approximately two orders of magnitude compared to the Generalized Cross-Correlation (GCC) method under disturbed channel conditions.
• Successfully resolved and tracked multiple propagation paths in real-world environments via field experiments.
• Demonstrated significant enhancement in measurement precision and reliability for HF sensing systems.

Conclusions:

• The MSIT architecture effectively transforms multipath interference into valuable observables.
• This novel approach significantly improves the performance of HF sensor networks in challenging ionospheric conditions.
• The MSIT method offers a reliable solution for accurate TOA estimation in dynamic HF environments.