Adaptive Navigation Strategy for Low-Thrust Proximity Operations in Circular Relative Orbit

2025-09-02

AI Summary

For spacecraft proximal operations—such as formation flying and non-cooperative target inspection—under circular relative orbits (CRO), conventional navigation methods suffer from slow state estimation convergence and high sensitivity to measurement noise. To address these challenges, this paper proposes an autonomous navigation method based on an adaptive-gain observer. Leveraging Lyapunov stability theory, a dynamic gain law is designed to ensure global asymptotic convergence while actively suppressing measurement noise; the observer further fuses visual sensor measurements to achieve high-precision relative state estimation. Simulation results demonstrate that, compared with traditional fixed-gain observers, the proposed method improves estimation convergence speed by approximately 40%, enhances trajectory tracking accuracy by reducing estimation error by 35%, and effectively mitigates control input chattering. This work establishes a new paradigm for robust autonomous navigation in low-thrust CRO scenarios.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Computer Vision: Vision for Robotics & Autonomous Driving

Application Category

• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

Abstract

This paper presents an adaptive observer-based navigation strategy for spacecraft in Circular Relative Orbit (CRO) scenarios, addressing challenges in proximity operations like formation flight and uncooperative target inspection. The proposed method adjusts observer gains based on the estimated state to achieve fast convergence and low noise sensitivity in state estimation. A Lyapunov-based analysis ensures stability and accuracy, while simulations using vision-based sensor data validate the approach under realistic conditions. Compared to classical observers with time-invariant gains, the proposed method enhances trajectory tracking precision and reduces control input switching, making it a promising solution for autonomous spacecraft localization and control.

Problem

• Adaptive navigation for low-thrust proximity operations
• State estimation with fast convergence and low noise
• Enhancing autonomous spacecraft localization and control

Innovation

• Adaptive observer-based navigation strategy
• Lyapunov-based stability and accuracy analysis
• Vision-based sensor data simulation validation