User performance analysis of a trajectory tracking task (Aerospace Implementation)

A comprehensive Python toolkit for analyzing aircraft flight paths, computing deviations from ideal trajectories, and quantifying flight smoothness metrics for training, safety, and research applications. Initially developed for Human-AI perception and teaming for a flight task with application potential including Autonomous driving and First Person Shooter Video Games.

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🌟 Features

• Decision Point Calculation – Compute waypoints along great‑circle routes for flight planning.
• Ideal Path Generation – Generate smooth, continuous 3D trajectories between defined waypoints.
• Flight Path Deviation Analysis – Measure 3D deviations between actual and ideal flight paths.
• Smoothness Metrics – Quantify flight quality using rate‑of‑change metrics.
• JASAT Waypoint Analysis – Detect closest approach and compare before/after behavior.
• Visualization Suite – Plots for paths, deviations, and statistical comparisons.

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📦 Installation

# Clone the repository
git clone https://github.com/mdmirah/flight-path-analysis.git
cd flight-path-analysis

# Install required packages
pip install numpy pandas matplotlib scipy geopy pyproj

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⚡ Quick Start

from flight_path_analysis import *

# Define waypoints (lat, lon, alt in feet)
waypoints = {
    "FEBAT": (37.485213, -118.413328, 11000),
    "JASAT": (37.6356, -118.5043, 11000),
    "RW27": (37.628611, -118.839167, 7140)
}

# 1. Compute a decision point 10 NM from FEBAT toward RW27
dp = compute_decision_point(waypoints["FEBAT"], waypoints["RW27"], distance_nm=10)

# 2. Generate ideal trajectory
ideal_path = plot_ideal_path(waypoints, total_path_points=1000)

# 3. Load aircraft data
# time, aircraft_lat, aircraft_lon, aircraft_alt = load_your_data()

# 4. Compute deviations
# deviation_df, jasat_info = flight_path_deviation_in_feet(
#     time, aircraft_lat, aircraft_lon, aircraft_alt,
#     ideal_path['Latitude'], ideal_path['Longitude'], ideal_path['Altitude']
# )

# 5. Analyze smoothness
# smoothness_df, stats = flight_smoothness(
#     time, deviation_df['total_deviation'], jasat_info['closest_time']
# )

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🧠 Core Functions

1. Decision Point Calculation

Function: compute_decision_point(before_dp, after_dp, distance_nm, plot=True)

Computes a waypoint a fixed distance along a great‑circle route.

Returns: (lat, lon)

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2. Ideal Path Generation

Function:
plot_ideal_path(waypoints, total_path_points=1000, plot=True)

Returns a dictionary with:

• Latitude
• Longitude
• Altitude

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3. Ideal vs Actual Path Overlay

Function:
ideal_vs_actual_path(...)

Produces a map overlay of ideal and real aircraft trajectory.

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4. Coordinate Conversion

Function:
lat_lon_alt_to_cartesian(lat, lon, alt)

Converts geographic coordinates into Earth‑centered Cartesian coordinates.

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5. Flight Path Deviation Analysis

Function:
flight_path_deviation_in_feet(...)

Computes:

• Total deviation
• N‑S deviation
• E‑W deviation
• Altitude deviation

Produces plots and JASAT segment analysis.

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6. Flight Smoothness Analysis

Function:
flight_smoothness(...)

Outputs smoothed deviation rate plots and statistical metrics.

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7. Position Comparison Plot

Function:
plot_flight_path_with_ideal_match_inputs(...)

Creates three aligned position plots (X, Y, Z).

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📊 Output Examples

✔ Decision Point Plot

Shows great‑circle path and computed decision point.

✔ Ideal Path

Altitude‑colored 3D trajectory.

✔ Deviation Analysis

Component‑wise deviation plots and before/after JASAT comparison.

✔ Smoothness

Rate‑of‑change plots and segment statistics.

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📁 Data Requirements

Aircraft data must include:

• Time (seconds, ideally 60 Hz)
• Latitude (deg)
• Longitude (deg)
• Altitude (ft)

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⚙️ Customization Options

Colormap

cmap = "plasma"

Smoothing Parameters

smoothing_times = {
    'x': 5.0,
    'y': 5.0,
    'z': 0.5
}

Waypoint Format

JASAT = (37.6356, -118.5043, 11000)
FEBAT = (37.485213, -118.413328, 11000)
RW27 = (37.628611, -118.839167, 7140)

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🎯 Use Cases

• Flight training evaluation
• Approach procedure development
• Safety event analysis
• Ride smoothness assessment
• Autonomy and trajectory research

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🧩 Dependencies

• NumPy
• Pandas
• Matplotlib
• SciPy
• GeoPy
• PyProj

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📂 Project Structure

flight-path-analysis/
│
├── flight_path_analysis.py
├── README.md
├── LICENSE
├── examples/
│   ├── basic_usage.py
│   └── complete_analysis.ipynb
└── data/
    └── sample_flight_data.csv

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🤝 Contributing

Pull Requests welcome!

Guidelines:

• PEP8 formatting
• Docstrings required
• Type hints recommended
• Add unit tests for new features

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📜 License

MIT License – see LICENSE file.

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📚 Citation

@inproceedings{rahman2026multimodal,
  title={A Multimodal Framework for Assessing Fitness to Fly in Flight Training},
  author={Rahman, Md Mijanur and Fala, Nicoletta},
  booktitle={AIAA Scitech 2026 Forum},
  pages={2734},
  year={2026},
  doi={10.2514/6.2026-2734}
}

@software{flight_path_analysis,
  author = {Md Mijanur Rahman},
  title = {Flight Path Analysis Tool},
  year = {2024},
  url = {https://github.com/mdmirah/Human-performance-analysis-of-a-trajectory-tracking-task}
}

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📬 Contact

Author: Md Mijanur Rahman Email: mzr0182@auburn.edu
GitHub: @mdmirah

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