Avionics-Propulsion-Landers-GT/MonopropUAV
Mar 2026 – Jul 2026
3 Months active
Languages Used
BashPythonRust
Technical Skills
3D animationKalman filteringPython programmingRust programmingalgorithm developmentdata analysis
aneesh-iyer29
PROFILE
Aneesh-iyer29
Over a three-month period, contributed to the Avionics-Propulsion-Landers-GT/MonopropUAV repository by developing advanced state estimation and simulation tools for UAV navigation and propulsion. Built a Rust-based 9-axis IMU Extended Kalman Filter with integrated Python visualization, enabling robust attitude, altitude, and position estimation validated against ground-truth data. Enhanced the simulation framework with object-oriented PID tuning and servo-driven valve models, supporting rapid parameter sweeps and hardware-in-the-loop readiness. Further improved navigation reliability by refining ES-EKF algorithms, integrating magnetometer and barometric data, and introducing Python-based 3D visualization tools. Emphasized modularity, automated testing, and thorough documentation to support maintainability and team collaboration.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
16Total
Bugs
0
Commits
16
Features
5
Lines of code
27,452
Activity Months3
Your Network
23 peopleShared Repositories
23
Work History
July 2026
4 Commits • 2 Features
Jul 1, 2026In July 2026, delivered two major enhancements to the MonopropUAV ES-EKF and introduced visualization tooling, driving improved navigation robustness and verifiability. Key outcomes include: 1) Enhanced ES-EKF with magnetometer fusion for yaw observability, VN-200 sensor integration, refined process noise modeling, and a fault-tolerant barometric altitude measurement with non-finite value rejection and chi-square gating, improving vertical positioning and filter robustness. 2) ES-EKF Visualization Tools: Python-based evaluation and animation for ES-EKF performance, ground-truth comparison, 3D plots, and improved output organization for plots, enabling faster validation and debugging.
4 Commits • 2 Features
Jul 1, 2026In July 2026, delivered two major enhancements to the MonopropUAV ES-EKF and introduced visualization tooling, driving improved navigation robustness and verifiability. Key outcomes include: 1) Enhanced ES-EKF with magnetometer fusion for yaw observability, VN-200 sensor integration, refined process noise modeling, and a fault-tolerant barometric altitude measurement with non-finite value rejection and chi-square gating, improving vertical positioning and filter robustness. 2) ES-EKF Visualization Tools: Python-based evaluation and animation for ES-EKF performance, ground-truth comparison, 3D plots, and improved output organization for plots, enabling faster validation and debugging.
July 2026
April 2026
4 Commits • 2 Features
Apr 1, 2026April 2026 — Two major simulation features delivered for MonopropUAV, enabling rapid verification and PID tuning for propulsion systems. Implemented Valworx 60° V-port valve servo-driven simulation with thrust calculations based on valve position and chamber pressure. Introduced a configurable PID tuning simulation framework for a hybrid rocket engine, including an extensible MTVSimulation class and a dedicated tuning runner. Achievements also include moving toward an OO, reusable simulation architecture, improving modularity and testability, and documenting code changes to support team collaboration. These deliverables strengthen validation capabilities, accelerate parameter sweeps and readiness for hardware-in-the-loop validation.
April 2026
4 Commits • 2 Features
Apr 1, 2026April 2026 — Two major simulation features delivered for MonopropUAV, enabling rapid verification and PID tuning for propulsion systems. Implemented Valworx 60° V-port valve servo-driven simulation with thrust calculations based on valve position and chamber pressure. Introduced a configurable PID tuning simulation framework for a hybrid rocket engine, including an extensible MTVSimulation class and a dedicated tuning runner. Achievements also include moving toward an OO, reusable simulation architecture, improving modularity and testability, and documenting code changes to support team collaboration. These deliverables strengthen validation capabilities, accelerate parameter sweeps and readiness for hardware-in-the-loop validation.
March 2026
8 Commits • 1 Features
Mar 1, 2026Month: 2026-03 | Repository: Avionics-Propulsion-Landers-GT/MonopropUAV Summary: - Delivered a robust 9-axis IMU EKF in Rust, establishing an end-to-end state estimation pipeline for attitude, altitude, and position with an integrated testing framework, ground-truth utilities, and Python-based visualization. Refactored EKF update logic and covariance handling to improve numerical stability and robustness to sensor noise; implemented gravity/magnetic reference refinements; added matrix-input support and an automated test/visualization script to streamline validation. - Fixed critical issues including gravity reference normalization and initial covariance handling, resulting in more reliable attitude estimation and faster validation cycles. - Created a scalable validation loop with automated scripts, enabling end-to-end testing against ground-truth data and visualization, reducing manual validation effort. - Prepared the foundation for future multi-sensor fusion and extendable state estimation, enabling safer UAV navigation and more accurate flight control decisions. Overall impact: - Business value: enhanced flight safety and mission reliability through accurate state estimation and faster validation; reduced risk from sensor noise and miscalibrations; accelerated development cycles for future sensor suites. - Technical achievements: Rust-based EKF, testing/visualization tooling, ground-truth integration, gravity/magnetic reference calibration, matrix-input support, and automated validation pipelines. Technologies/skills demonstrated: - Rust, EKF/state estimation, numerical stability improvements, sensor fusion concepts; Python-based visualization; automated testing scripts; ground-truth data integration; refactoring for maintainability and performance.
8 Commits • 1 Features
Mar 1, 2026Month: 2026-03 | Repository: Avionics-Propulsion-Landers-GT/MonopropUAV Summary: - Delivered a robust 9-axis IMU EKF in Rust, establishing an end-to-end state estimation pipeline for attitude, altitude, and position with an integrated testing framework, ground-truth utilities, and Python-based visualization. Refactored EKF update logic and covariance handling to improve numerical stability and robustness to sensor noise; implemented gravity/magnetic reference refinements; added matrix-input support and an automated test/visualization script to streamline validation. - Fixed critical issues including gravity reference normalization and initial covariance handling, resulting in more reliable attitude estimation and faster validation cycles. - Created a scalable validation loop with automated scripts, enabling end-to-end testing against ground-truth data and visualization, reducing manual validation effort. - Prepared the foundation for future multi-sensor fusion and extendable state estimation, enabling safer UAV navigation and more accurate flight control decisions. Overall impact: - Business value: enhanced flight safety and mission reliability through accurate state estimation and faster validation; reduced risk from sensor noise and miscalibrations; accelerated development cycles for future sensor suites. - Technical achievements: Rust-based EKF, testing/visualization tooling, ground-truth integration, gravity/magnetic reference calibration, matrix-input support, and automated validation pipelines. Technologies/skills demonstrated: - Rust, EKF/state estimation, numerical stability improvements, sensor fusion concepts; Python-based visualization; automated testing scripts; ground-truth data integration; refactoring for maintainability and performance.
March 2026
Activity
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Quality Metrics
Correctness90.0%
Maintainability87.6%
Architecture86.2%
Performance85.0%
AI Usage36.2%
Skills & Technologies
Programming Languages
BashPythonRust
Technical Skills
3D animationAlgorithm developmentCommenting and documentationControl SystemsData AnalysisData VisualizationEmbedded SystemsKalman FilteringKalman filteringLinear AlgebraMatplotlibNumPyPID controlPandasPython
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