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
Worked on the NCSU-High-Powered-Rocketry-Club/Payload-2024-2025 repository, delivering foundational improvements to IMU data handling, flight data packet formats, and sensor fusion pipelines. Overhauled the IMU architecture with a generic data packet system and lifecycle management, modernized data serialization, and introduced robust mock data generation for reliable testing. Implemented quaternion-based orientation estimation with AHRS, Madgwick, and Davenport filters, expanding validation across diverse sensor inputs. Enhanced code quality through targeted cleanup, explicit error signaling, and test suite maintenance. Leveraged C++ and Python for embedded systems, sensor integration, and scientific computing, resulting in improved data integrity, maintainability, and test coverage.
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
33 peopleSame Organization
@ncsu.edu23
Abel LuMember
Alli WrayMember
Bradon HassellMember
cnswans2Member
connors-01Member
csdechantMember
Casey IcenhourMember
Daniel HenningerMember
Dhruvil ShahMember
Shared Repositories
10
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 Completed code cleanup and test-suite maintenance (removing unused variables/debug prints, deleting outdated files, and pruning the rolling average window) to improve readability, stability, and maintainability.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
Loading activity data...
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
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline