Genetic Optimization of a Software-Defined GNSS Receiver

• URL: http://arxiv.org/abs/2510.22417v1
• Date: Sat, 25 Oct 2025 19:32:32 GMT
• Title: Genetic Optimization of a Software-Defined GNSS Receiver
• Abstract summary: 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.<n>Results demonstrate that evolutionary optimization enables SDR receivers to maintain robust and accurate Position, Velocity, and Time (PVT) solutions across diverse dynamic conditions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• A Neural Network Based Teleoperation for Remote Controlled Vehicles [98.38733381483934]
  Direct teleoperation of vehicles faces critical technical bottlenecks.<n> communication latency and the operator's inability to physically perceive unmodeled environmental disturbances.<n>We propose a tailored unilateral teleoperation framework.
  arXiv  Detail & Related papers  (2026-08-11T01:51:08Z)
• Empowering a Single-Frequency GNSS Receiver to Achieve High-Precision Positioning with Relative Observations [31.54785319181002]
  Real-Time Kinematic (RTK) technology can achieve centimeter-level accuracy, facilitating autonomous navigation tasks.<n>We present a novel tightly-coupled state estimation framework that achieves high-precision localization.<n>Our method improves the accuracy of a single-frequency receiver from several meters to decimeter-level precision across diverse environments.
  arXiv  Detail & Related papers  (2026-06-28T04:30:01Z)
• LiDAR Teach, Radar Repeat: Robust Cross-Modal Navigation in Degenerate and Varying Environments [11.139057813795828]
  Teach-and-Repeat (T&R) navigation offers a reliable and cost-effective solution by avoiding the need for consistent global mapping.<n>Existing T&R systems lack a systematic solution to tackle various environmental variations such as weather degradation, ephemeral dynamics, and structural changes.<n>This work proposes LTR$2$, the first cross-modal, cross-platform LiDAR-Teach-and-Radar-Repeat system.
  arXiv  Detail & Related papers  (2026-05-04T16:46:54Z)
• RAD-2: Scaling Reinforcement Learning in a Generator-Discriminator Framework [49.531001563853984]
  RAD-2 is a unified generator-discriminator framework for closed-loop planning.<n>It produces diverse trajectory candidates, while an RL-optimized discriminator reranks these candidates according to their long-term driving quality.<n>It reduces the collision rate by 56% compared with strong diffusion-based planners.
  arXiv  Detail & Related papers  (2026-04-16T17:59:44Z)
• HAD: Combining Hierarchical Diffusion with Metric-Decoupled RL for End-to-End Driving [51.268878540511054]
  We propose HAD, an end-to-end planning framework with a Hierarchical Diffusion Policy.<n>We show that HAD achieves new state-of-the-art performance on both NAVSIM and HUGSIM.
  arXiv  Detail & Related papers  (2026-04-04T04:12:47Z)
• Spatially-Aware Adaptive Trajectory Optimization with Controller-Guided Feedback for Autonomous Racing [74.83272587893508]
  We present a framework for autonomous raceline optimization that combines NURBS-based trajectory representation, CMA-ES global trajectory optimization, and controller-guided spatial feedback.<n>In simulation, our approach achieves a 17.38% lap time reduction compared to a controller parametrized with maximum static acceleration.<n>On real hardware, tested with different tire compounds ranging from high to low friction, we obtain a 7.60% lap time improvement without explicitly parametrizing friction.
  arXiv  Detail & Related papers  (2026-02-17T15:10:44Z)
• Reinforcement Learning for Opportunistic Routing in Software-Defined LEO-Terrestrial Systems [16.049887428366333]
  Large-scale low Earth orbit (LEO) satellite constellations are driving the need for intelligent routing strategies.<n>We introduce opportunistic routing, which aims to minimize delivery delay by forwarding packets to any currently available ground gateways rather than fixed destinations.
  arXiv  Detail & Related papers  (2026-01-20T07:01:14Z)
• Heteroscedastic Bayesian Optimization-Based Dynamic PID Tuning for Accurate and Robust UAV Trajectory Tracking [12.655695094805159]
  HBO-PID is a novel control algorithm that integrates the Heteroscedastic Bayesian Optimization framework with the classical controller.<n>It improves the position accuracy by 24.7% to 42.9%, and the angular accuracy by 40.9% to 78.4%.
  arXiv  Detail & Related papers  (2025-12-30T14:21:17Z)
• Nemotron-Flash: Towards Latency-Optimal Hybrid Small Language Models [97.55009021098554]
  This work aims to identify the key determinants of SLMs' real-device latency and offer generalizable principles and methodologies for SLM design and training.<n>We introduce a new family of hybrid SLMs, called Nemotron-Flash, which significantly advances the accuracy-efficiency frontier of state-of-the-art SLMs.
  arXiv  Detail & Related papers  (2025-11-24T08:46:36Z)
• Stable Multi-Drone GNSS Tracking System for Marine Robots [7.911692711262891]
  We present a scalable multi-drone-based tracking system for surface and near-surface marine robots.<n>Our approach combines efficient visual detection, lightweight multi-object tracking, and a confidence-weighted Extended Kalman Filter (EKF) to provide stable estimation in real time.
  arXiv  Detail & Related papers  (2025-11-24T02:28:31Z)
• SWA-PF: Semantic-Weighted Adaptive Particle Filter for Memory-Efficient 4-DoF UAV Localization in GNSS-Denied Environments [8.46731803518948]
  Vision-based Unmanned Aerial Vehicle (UAV) localization systems have been extensively investigated for Global Navigation Satellite System (GNSS)-denied environments.<n>We present a large-scale Multi-Altitude Flight Segments dataset (MAFS) for variable altitude scenarios.<n>We propose a novel Semantic-Weighted Adaptive Particle Filter (SWA-PF) method to overcome these limitations.
  arXiv  Detail & Related papers  (2025-09-17T08:05:36Z)
• Joint AoI and Handover Optimization in Space-Air-Ground Integrated Network [48.485907216785904]
  Low Earth orbit (LEO) satellite constellations offer promising solutions with global coverage and reduced latency.<n>Yet struggle with intermittent coverage and intermittent communication windows due to orbital dynamics.<n>Our three-layer design employs hybrid free-space optical (FSO) links for high-capacity satellite-to-ground communication and reliable radio frequency (RF) links for HAP-to-ground transmission.
  arXiv  Detail & Related papers  (2025-09-16T06:16:56Z)
• Data-Driven Antenna Miniaturization: A Knowledge-Based System Integrating Quantum PSO and Predictive Machine Learning Models [0.0]
  This study integrates Quantum-Behaved Dynamic Particle Swarm Optimization with HFSS simulations to accelerate antenna design.<n>QDPSO algorithm autonomously optimized loop dimensions in 11.53 seconds, achieving a resonance frequency of 1.4208 GHz.<n>System enables precise specifications of performance targets with automated generation of fabrication-ready parameters.
  arXiv  Detail & Related papers  (2025-05-28T15:04:36Z)
• Gaussian Process Regression for Improved Underwater Navigation [13.221163846643607]
  Doppler velocity logs (DVLs) are typically used to mitigate this drift through velocity measurements.<n>This paper proposes a data-driven alternative based on multi-output Gaussian process regression (MOGPR) to improve DVL velocity estimation.<n>We evaluate our proposed approach using real-world AUV data and compare it against LS and a state-of-the-art deep learning model, BeamsNet.
  arXiv  Detail & Related papers  (2025-02-23T09:13:41Z)
• Low-altitude Friendly-Jamming for Satellite-Maritime Communications via Generative AI-enabled Deep Reinforcement Learning [72.72954660774002]
  Low Earth Orbit (LEO) satellites can be used to assist maritime wireless communications for data transmission across wide-ranging areas.<n>Extensive coverage of LEO satellites, combined with openness of channels, can cause the communication process to suffer from security risks.<n>This paper presents a low-altitude friendly-jamming LEO satellite-maritime communication system enabled by a unmanned aerial vehicle.
  arXiv  Detail & Related papers  (2025-01-26T10:13:51Z)
• Learning to Initialize Trajectory Optimization for Vision-Based Autonomous Flight in Unknown Environments [4.0543433786183485]
  We present a Neural-Enhanced Tray Planner (NEO-Planner) for autonomous flight in unknown environments.<n>NEO-Planner learns to predict spatial and temporal parameters for trajectories directly from raw sensor observations.<n>It reduces optimization by 20%, leading to a 26% decrease in trajectory time compared with pure optimization-based methods.
  arXiv  Detail & Related papers  (2023-09-19T15:07:26Z)
• Automated classification of pre-defined movement patterns: A comparison between GNSS and UWB technology [55.41644538483948]
  Real-time location systems (RTLS) allow for collecting data from human movement patterns. The current study aims to design and evaluate an automated framework to classify human movement patterns in small areas.
  arXiv  Detail & Related papers  (2023-03-10T14:46:42Z)
• Distributed Multi-agent Meta Learning for Trajectory Design in Wireless Drone Networks [151.27147513363502]
  This paper studies the problem of the trajectory design for a group of energyconstrained drones operating in dynamic wireless network environments. A value based reinforcement learning (VDRL) solution and a metatraining mechanism is proposed.
  arXiv  Detail & Related papers  (2020-12-06T01:30:12Z)
• Pushing the Envelope of Rotation Averaging for Visual SLAM [69.7375052440794]
  We propose a novel optimization backbone for visual SLAM systems. We leverage averaging to improve the accuracy, efficiency and robustness of conventional monocular SLAM systems. Our approach can exhibit up to 10x faster with comparable accuracy against the state-art on public benchmarks.
  arXiv  Detail & Related papers  (2020-11-02T18:02:26Z)
• Integrating LEO Satellite and UAV Relaying via Reinforcement Learning for Non-Terrestrial Networks [51.05735925326235]
  A mega-constellation of low-earth orbit (LEO) satellites has the potential to enable long-range communication with low latency. We study the problem of forwarding packets between two faraway ground terminals, through an LEO satellite selected from an orbiting constellation. To maximize the end-to-end data rate, the satellite association and HAP location should be optimized. We tackle this problem using deep reinforcement learning (DRL) with a novel action dimension reduction technique.
  arXiv  Detail & Related papers  (2020-05-26T05:39:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.