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Radiation Pattern Classification Using Machine Learning on Public Antenna Datasets

May 27, 2025
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Project Description

Create a machine learning model that classifies antenna radiation patterns (e.g., omnidirectional, directional, patch, helical) using publicly available antenna radiation data.

The goal is to build a system that learns pattern characteristics from graphs or numerical data and classifies them into predefined antenna types — useful for educational or pattern recognition tools.

No dataset is provided by the client — the freelancer is expected to find or generate simulated data from open-access sources.

🎯 Objectives:

  1. Collect or generate a dataset of at least 100 radiation patterns (2D polar or 3D Cartesian): Can use open repositories, academic papers, or simulate via tools like Python, MATLAB. Dataset should label patterns by antenna type (e.g., dipole, Yagi, patch, horn)
  2. Preprocess data into a usable ML format: Extract key features: beamwidth, side lobes, front-to-back ratio, etc. Optionally convert plots into vector features or images.
  3. Train a machine learning model to classify the radiation patterns
  4. Evaluate accuracy with confusion matrix and performance metrics
  5. Document your work with: Dataset source and cleaning notes, Model training steps, Results and limitations, Suggestions for future improvements.

 

📦 Deliverables:

  • Python scripts or Jupyter notebooks
  • ML model file (e.g., .pkl or TensorFlow/PyTorch)
  • The dataset (with sources)
  • Final PDF report (6–10 pages) including:
    • Dataset overview
    • Classification approach
    • Evaluation metrics
    • Visual results

🛠 Preferred Tools:

  • Python (scikit-learn, TensorFlow, matplotlib, pandas)
  • Optional: MATLAB or GNU Radio for simulation
  • Data sources: IEEE DataPort, Kaggle, or self-simulated with NEC2

💼 Ideal Freelancer Background:

  • Experience with machine learning for classification tasks
  • Familiarity with antenna theory and radiation patterns

📥 How to Apply:
Must be include:

Example of ML or signal/data classification work you’ve done.
How you plan to build or find the dataset.
Your preferred tech stack and training method.

Send Your Proposal