Genetic Optimization of a Software-Defined GNSS Receiver

2025-10-25

AI Summary

Fixed-bandwidth tracking loops in commercial GNSS receivers lead to discontinuous and degraded PVT solutions under high-dynamic conditions (e.g., rocket launch, LEO satellite motion). To address this, this paper proposes a GA-assisted adaptive tracking loop optimization method integrated within a GNSS software-defined radio (GNSS-SDR) framework. Leveraging genetic algorithms, the approach autonomously searches for globally optimal parameter combinations across phase, frequency, and code delay loops—overcoming limitations of manual tuning—and enables real-time reconfiguration and robust signal tracking in dynamic environments. Experimental validation across static, rocket flight, and LEO satellite scenarios demonstrates position errors of ≤6 m, 12 m, and 5 m, respectively, and velocity errors consistently below 2.5 m/s. The method significantly enhances both stability and accuracy of high-dynamic GNSS navigation solutions.

Technology Category

• Intelligent Robots: State Estimation
• Search and Optimization: Mixed Discrete/Continuous Search
• Computer Vision: Motion & Tracking

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Tracking, profiling, and countermeasures against them
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

Commercial off-the-shelf (COTS) Global Navigation Satellite System (GNSS) receivers face significant limitations under high-dynamic conditions, particularly in high-acceleration environments such as those experienced by launch vehicles. These performance degradations, often observed as discontinuities in the navigation solution, arise from the inability of traditional tracking loop bandwidths to cope with rapid variations in synchronization parameters. Software-Defined Radio (SDR) receivers overcome these constraints by enabling flexible reconfiguration of tracking loops; however, manual tuning involves a complex, multidimensional search and seldom ensures optimal performance. This work introduces a genetic algorithm-based optimization framework that autonomously explores the receiver configuration space to determine optimal loop parameters for phase, frequency, and delay tracking. The approach is validated within an SDR environment using realistically simulated GPS L1 signals for three representative dynamic regimes -guided rocket flight, Low Earth Orbit (LEO) satellite, and static receiver-processed with the open-source GNSS-SDR architecture. Results demonstrate that evolutionary optimization enables SDR receivers to maintain robust and accurate Position, Velocity, and Time (PVT) solutions across diverse dynamic conditions. The optimized configurations yielded maximum position and velocity errors of approximately 6 m and 0.08 m/s for the static case, 12 m and 2.5 m/s for the rocket case, and 5 m and 0.2 m/s for the LEO case.

Problem

• Optimizing GNSS receiver tracking loops for high-dynamic conditions
• Automating loop parameter tuning using genetic algorithms
• Enhancing PVT accuracy across diverse dynamic environments

Innovation

• Genetic algorithm optimizes GNSS receiver tracking loops
• Autonomously explores configuration space for optimal parameters
• Enables robust PVT solutions across dynamic conditions