Localization and ambiguity resolution algorithm for time-difference fusion of three satellites based on observation...

Area of Science:

• Satellite Navigation
• Signal Processing
• Estimation Theory

Background:

• Traditional satellite localization methods suffer from low accuracy and time-difference ambiguity.
• Accurate multi-satellite time-difference fusion is crucial for robust positioning and tracking.

Purpose of the Study:

• To develop advanced algorithms for multi-satellite time-difference fusion localization and tracking.
• To address and resolve issues of low accuracy and time-difference ambiguity in satellite positioning.

Main Methods:

• A Gaussian-Newton iteration-based localization algorithm is proposed, utilizing time-difference and elevation observations.
• Kalman filtering combined with Gaussian mixture models (KFGMM and CKFGMM) are developed for ambiguity resolution in stationary and cruising targets.
• A mathematical model for time-difference ambiguity is established, with methods for calculating time-difference windows and approximating measurements.

Main Results:

• The proposed localization algorithm achieves the Cramér-Rao Lower Bound (CRLB) for fusion localization, outperforming existing methods.
• Ambiguity resolution algorithms approach the Bayesian Cramér-Rao Lower Bound (BCRLB) with increased filtering time.
• The developed algorithms demonstrate superior performance compared to direction-finding assisted methods.

Conclusions:

• The novel algorithms significantly enhance the accuracy and reliability of multi-satellite localization and tracking.
• The proposed methods effectively resolve time-difference ambiguity, crucial for precise satellite navigation.
• This research contributes to the advancement of satellite-based positioning systems.