High-Accuracy Positioning Based on Pseudo-Ranges: Integrated Difference and Performance Analysis of the Loran System

Area of Science:

• Navigation Systems
• Geomatics Engineering
• Signal Processing

Background:

• Global Navigation Satellite System (GNSS) accuracy is limited by pseudo-range errors and geometric dilution of precision (GDOP).
• The Long Range Navigation (Loran) system presents a potential backup for GNSS, requiring methods to improve its positioning accuracy.
• Understanding pseudo-range measurement characteristics is crucial for enhancing navigation system performance.

Purpose of the Study:

• To develop and validate an algorithm that integrates pseudo-range differences to improve Loran positioning accuracy.
• To analyze the impact of systematic errors and random noise on absolute and repeatable accuracy in Loran positioning.
• To demonstrate how increasing transmitter numbers can reduce GDOP and enhance differential performance.

Main Methods:

• Comparison of theoretical propagation delay with raw pseudo-range measurements to identify constant and temporal terms.
• Development of a position solution algorithm utilizing pseudo-range differences, extending capabilities beyond single-chain limitations.
• Simulation tests to evaluate the differential performance of the proposed algorithm and its impact on GDOP.

Main Results:

• Pseudo-range measurements contain constant and temporal terms reflecting propagation conditions.
• The proposed pseudo-range difference algorithm effectively improves position accuracy and can reduce GDOP by increasing transmitters.
• Distinguishing systematic errors (affecting absolute accuracy) and random noise (affecting repeatable accuracy) is key. The difference method enhances absolute accuracy without altering transmitter geometry.

Conclusions:

• The integrated pseudo-range difference algorithm offers a robust method for enhancing Loran positioning accuracy.
• Loran can serve as a reliable GNSS backup, particularly when its positioning accuracy is optimized through advanced algorithms.
• The methodology effectively addresses both systematic biases and random noise, leading to improved navigation performance.