# Adaptive Trajectory-Constrained Heading Estimation for Tractor GNSS/SINS Integrated Navigation

## TL;DR

A new method for tractor navigation improves heading accuracy and reduces convergence time using a single-antenna system, making autonomous farming more practical.

## Contribution

An adaptive trajectory-constrained heading estimation method using SWAEKF is proposed for low-speed tractor navigation.

## Key findings

- The method achieves rapid convergence (<10 s for straight lines) and high accuracy (RMS heading error <0.15°).
- Field tests showed a 23% improvement in heading accuracy and 62% reduction in convergence time compared to conventional adaptive EKF.

## Abstract

What are the main findings?
An adaptive trajectory-constrained heading estimation method using SWAEKF is proposed, achieving rapid convergence (<10 s for straight lines) and high accuracy (RMS heading error <0.15°) in low-speed tractor navigation.A 23% improvement in heading accuracy and 62% reduction in convergence time compared to conventional adaptive EKF are demonstrated through field tests, including straight and curved paths.

An adaptive trajectory-constrained heading estimation method using SWAEKF is proposed, achieving rapid convergence (<10 s for straight lines) and high accuracy (RMS heading error <0.15°) in low-speed tractor navigation.

A 23% improvement in heading accuracy and 62% reduction in convergence time compared to conventional adaptive EKF are demonstrated through field tests, including straight and curved paths.

What are the implications of the main findings?
A cost-effective solution is provided for the autonomous navigation of small-to-medium tractors by enabling precise heading estimation with single-antenna GNSS/SINS integration, reducing reliance on dual-antenna systems.Adaptability to low-dynamic farmland environments is enhanced, supporting the advancement of agricultural automation with robust sensor fusion.

A cost-effective solution is provided for the autonomous navigation of small-to-medium tractors by enabling precise heading estimation with single-antenna GNSS/SINS integration, reducing reliance on dual-antenna systems.

Adaptability to low-dynamic farmland environments is enhanced, supporting the advancement of agricultural automation with robust sensor fusion.

Accurate heading estimation is crucial for the autonomous navigation of small-to-medium tractors. While dual-antenna GNSS systems offer precision, they face installation and safety challenges. Single-antenna GNSS integrated with a low-cost Strapdown Inertial Navigation System (SINS) presents a more adaptable solution but suffers from slow convergence and low accuracy of heading estimation in low-speed farmland operations. This study proposes an adaptive trajectory-constrained heading estimation method. A sliding-window adaptive extended Kalman filter (SWAEKF) was developed, incorporating a heading constraint model that utilizes the GNSS-derived trajectory angle. An enhanced Sage–Husa algorithm was employed for the adaptive estimation of the trajectory angle measurement variance. Furthermore, a covariance initialization strategy based on the variance of trajectory angle increments was implemented to accelerate convergence. Field tests demonstrated that the proposed method achieved rapid heading convergence (less than 10 s for straight lines and 14 s for curves) and high accuracy (RMS heading error below 0.15° for straight-line tracking and 0.25° for curved paths). Compared to a conventional adaptive EKF, the SWAEKF improved accuracy by 23% and reduced convergence time by 62%. The proposed algorithm effectively enhances the performance of GNSS/SINS integrated navigation for tractors in low-dynamic environments, meeting the requirements for autonomous navigation systems.