NCSU-High-Powered-Rocketry-Club/Payload-2024-2025
Jan 2025 – Feb 2025
2 Months active
Languages Used
C++CSVPythonTOML
Technical Skills
C++Class DesignData AcquisitionData EngineeringData LoggingData Processing
Will Sanderson
PROFILE
Will Sanderson
Will Sanders contributed to the NCSU-High-Powered-Rocketry-Club/Payload-2024-2025 repository by refactoring IMU data handling and modernizing flight data packet formats to improve data integrity and testability. He implemented a generic IMUDataPacket structure and lifecycle management, enabling reliable processing for both real and simulated flights. Sanders overhauled orientation estimation using quaternion mathematics, integrating AHRS, Madgwick, and Davenport filters for robust sensor fusion. He also introduced comprehensive mock data tooling and expanded unit tests, ensuring validation across diverse sensor inputs. Working primarily in C++ and Python, Sanders focused on code cleanup, maintainability, and embedded systems, resulting in a more reliable and extensible codebase.
Overall Statistics
Feature vs Bugs
83%Features
Repository Contributions
30Total
Bugs
1
Commits
30
Features
5
Lines of code
11,677
Activity Months2
Your Network
28 people
Work History
February 2025
18 Commits • 2 Features
Feb 1, 2025February 2025: Delivered a robust IMU orientation overhaul with quaternion-based RPY extraction, integrated AHRS/Madgwick/Davenport filters, and expanded validation tests across diverse sensor inputs. Implemented explicit signaling for incomplete functionality in Transmitter.start (NotImplementedError) to prevent silent runtime failures. Completed code cleanup and test-suite maintenance (removing unused variables/debug prints, deleting outdated files, and pruning the rolling average window) to improve readability, stability, and maintainability. Overall impact: more reliable attitude data, reduced risk from partial features, and a cleaner codebase enabling faster future iterations. Technologies/skills demonstrated: embedded sensor fusion, quaternion math, multiple filter implementations, unit testing, and code hygiene/CI-quality improvements.
18 Commits • 2 Features
Feb 1, 2025February 2025: Delivered a robust IMU orientation overhaul with quaternion-based RPY extraction, integrated AHRS/Madgwick/Davenport filters, and expanded validation tests across diverse sensor inputs. Implemented explicit signaling for incomplete functionality in Transmitter.start (NotImplementedError) to prevent silent runtime failures. Completed code cleanup and test-suite maintenance (removing unused variables/debug prints, deleting outdated files, and pruning the rolling average window) to improve readability, stability, and maintainability. Overall impact: more reliable attitude data, reduced risk from partial features, and a cleaner codebase enabling faster future iterations. Technologies/skills demonstrated: embedded sensor fusion, quaternion math, multiple filter implementations, unit testing, and code hygiene/CI-quality improvements.
February 2025
January 2025
12 Commits • 3 Features
Jan 1, 2025January 2025 -- Key accomplishments in Payload-2024-2025: delivered a foundational IMU data handling refactor with lifecycle management, modernized the flight data packet format for improved fidelity, and expanded data acquisition tooling with robust mock data generation. These changes improve data integrity, enable reliable testing across real and simulated flights, and set the stage for more accurate telemetry analytics and faster validation cycles.
January 2025
12 Commits • 3 Features
Jan 1, 2025January 2025 -- Key accomplishments in Payload-2024-2025: delivered a foundational IMU data handling refactor with lifecycle management, modernized the flight data packet format for improved fidelity, and expanded data acquisition tooling with robust mock data generation. These changes improve data integrity, enable reliable testing across real and simulated flights, and set the stage for more accurate telemetry analytics and faster validation cycles.
Activity
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Quality Metrics
Correctness84.6%
Maintainability84.0%
Architecture80.6%
Performance77.4%
AI Usage20.6%
Skills & Technologies
Programming Languages
C++CSVPythonTOML
Technical Skills
C++Class DesignCode CleanupCode RefactoringConfigurationData AcquisitionData EngineeringData LoggingData ProcessingData SerializationData SimulationEmbedded SystemsFile ManagementFlight Data AnalysisHardware Integration
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