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

## TL;DR

This paper introduces a new method to improve time-of-arrival estimation in HF sensor networks by treating multipath interference as useful data.

## Contribution

The novel MSIT framework transforms multipath interference into observables for more accurate delay estimation.

## Key findings

- The MSIT method reduces mean delay estimation error by two orders of magnitude compared to GCC in simulations.
- Field experiments on the Xi’an–Ürümqi link show stable resolution and tracking of multiple propagation paths.
- The approach significantly improves precision and reliability in HF sensing systems under disturbed conditions.

## Abstract

High-frequency (HF) sensor networks play an irreplaceable role in remote sensing and emergency communications but suffer severely from ionospheric multipath interference, which degrades Time-of-Arrival (TOA) estimation accuracy. Conventional methods, such as the Generalized Cross-Correlation (GCC) and standard Delay-Locked Loops (DLL), often treat multipath components as noise, leading to significant measurement bias in dynamic environments. To address this, we propose a Multipath Separation and Independent Tracking (MSIT) architecture. This framework transforms multipath interference into valuable observables by establishing a closed-loop synergy: a Maximum Likelihood Estimation (MLE)-based module iteratively separates signal components, while parallel tracking loops update phase and delay parameters. Additionally, a super-resolution MUSIC algorithm is employed for initialization to resolve sub-chip multipath components. Simulations demonstrate that under disturbed channel conditions, the MSIT method achieves a mean delay estimation error reduction of about two orders of magnitude relative to the GCC method. Furthermore, field experiments on the Xi’an–Ürümqi link demonstrate its capability to stably resolve and track multiple propagation paths in real-world environments. This approach significantly enhances the measurement precision and reliability of HF sensing systems.