Create Validation and Testing Strategy for FMCW Implementation

[davidbits]

The FMCW implementation is a major new feature that requires rigorous validation to ensure it produces physically accurate results. The testing strategy must go beyond simple code-level checks and include validation against theoretical models and real-world data.

Tasks:

1. Unit Tests:

   • Write unit tests for the new de-chirping mixer module.
   • Test cases should include:
     • A static target at a known range, verifying the output beat frequency.
     • A moving target with a known velocity, verifying the Doppler shift on the beat frequency.
     • A test to confirm that phase noise from a mock timing source is correctly passed into the IF signal.

2. Integration/Regression Tests:

   • Create new test cases in the test/sim_tests/ directory for simple FMCW scenarios.
   • Test Case 1 (Static Target): A single, static target. The expected output should be an HDF5 file containing a constant-frequency complex sinusoid (the IF signal).
   • Test Case 2 (Moving Target): A single target moving at a constant velocity. The expected output should reflect the correct beat frequency and Doppler shift.
   • These tests will validate the entire chain from XML parsing to IF signal generation.

3. Physical Validation:

   • Data Gathering: Set up a real-world test with a FMCW radar sensor. Record the raw IF data for a simple, controlled scenario (e.g., a corner reflector at a measured distance).
   • Simulation Replication: Create a FERS .fersxml scenario that precisely matches the parameters of the physical test (antenna positions, target distance, chirp settings).
   • Comparison: Develop Python/MATLAB scripts to compare the output of the FERS simulation with the physically captured data. The analysis should focus on matching the beat frequency, SNR, and phase noise characteristics.

Acceptance Criteria:

• All unit tests for the FMCW mixer pass.
• New regression tests for simple FMCW scenarios are added and pass.
• A validation report is produced that demonstrates a high degree of correlation between FERS-simulated IF data and data captured from a physical FMCW system.