AAE6102 Satellite Communication and Navigation Lab - Playing with Opensourced Tools

Hong Kong Polytechnic University

Department of Aeronautical and Aviation Engineering
2024/25 Semester 2

• Laboratory Date: 27th March 2025, 15:30-18:20
• Lab Report Due Date: Before 17 April 2025

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Objective

The objective of this lab is to explore open-source GNSS libraries, evaluate their performance using publicly available datasets, and analyze their strengths, limitations, and comparative effectiveness through parameter tuning and evaluation.

Repository: AAE6102 Laboratory GitHub Repository

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1. Submission Requirements

• The lab report and results should be submitted as a group-created GitHub repository.
• The report must be written in the README.md file.
• Refer to the following links for README formatting:
  • GitHub README Guide
  • Basic Writing and Formatting on GitHub

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2. Testing GNSS Libraries

2.1 Select a GNSS Library

Choose one open-source GNSS library from the following examples:

• RTKLIB (C/C++, Python wrapper, MATLAB wrapper):
  • Original version (C/C++)
  • Optimized version (C/C++)
  • Python wrapper 1
  • Python wrapper 2
  • MATLAB wrapper
  • MATLAB with GTSAM
• GoGPS (MATLAB): GitHub Repository
• GICI (C/C++): GitHub Repository

2.2 Exploring the Library

• Review the functionality provided by the library.
• Identify key components such as:
  • Algorithms used
  • Inputs and outputs
  • Parameters available for tuning

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3. Testing Open-Source Datasets with the Selected GNSS Library

3.1 Select a Dataset

Choose one or more datasets for testing:

• UrbanNav Dataset: GitHub Repository
• Google Smartphone Decimeter Challenge: Kaggle Competition

3.2 Process the Dataset

• Use the GNSS library to process the selected dataset.
• Generate positioning results such as:
  • Trajectory visualization
  • Error analysis
  • Signal quality analysis

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4. Parameter Tuning and Performance Evaluation

4.1 Tune Parameters

Identify and modify tunable parameters, such as:

• Positioning Mode: Kinematic/Static, Single/Differential
• Filter Settings: Kalman Filter parameters
• Satellite Selection Criteria: Signal-to-noise ratio thresholds

Observe the effects of parameter changes on results.

4.2 Analyze Results

• Compare results before and after tuning.
• Evaluate performance metrics:
  • Positioning accuracy
  • Processing time
  • Robustness to challenging conditions

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5. Lab Report Preparation

Prepare a detailed lab report covering the following aspects:

5.1 Parameter Tuning and Effects

• List the parameters tuned.
• Explain how parameter changes affected:
  • Accuracy
  • Processing speed
  • Robustness

5.2 Strengths and Limitations

• Strengths: Flexibility, robustness, ease of use.
• Limitations: Computational efficiency, lack of specific features.

5.3 Kaggle competition

Submit your positioning result and beat your classmate at Kaggle competition

5.4 Comparison with Other Libraries (Optional)

If time permits, test another GNSS library using the same dataset and compare based on:

• Accuracy
• Ease of use
• Flexibility
• Computational efficiency

5.5 Suggestions for Improvement

Provide recommendations to enhance:

• The selected GNSS library
• The parameter tuning process

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References

• AAE6102 Laboratory Repository
• UrbanNav Dataset
• Google Smartphone Decimeter Challenge
• GitHub README Guide
• Basic GitHub Formatting

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End of README