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Area of Science:

  • Signal Processing
  • Machine Learning
  • Radar Systems

Background:

  • Orthogonal Time-Frequency Space (OTFS) modulation offers advantages in high-Doppler environments.
  • Accurate target parameter extraction is crucial for sensing applications.

Purpose of the Study:

  • To develop an end-to-end pipeline for OTFS sensing integrating deterministic signal processing and machine learning.
  • To achieve precise extraction of physical target parameters (range, radial velocity, amplitude, phase) using OTFS.

Main Methods:

  • A pipeline combining Symplectic Fast Fourier Transform (SFFT)-based OTFS reception with an 'oracle' Ground-Truth (GT) association process.
  • Training a Random-Forest (RF) classifier on normalized complex patches of signal peaks for target parameter mapping.
  • Utilizing a hybrid approach of deterministic processing and ML inference.

Main Results:

  • The RF classifier achieved high accuracy (0.966), macro-F1 score (0.965), and ROC-AUC (0.998) on training data.
  • The model demonstrated 100% coincidence for range and velocity predictions on unseen data.
  • Amplitude and phase correspondence with GT reached 89%.

Conclusions:

  • The proposed hybrid oracle-and-ML pipeline is a robust and effective method for precise target extraction in OTFS sensing.
  • This approach significantly enhances the performance of OTFS-based sensing systems for target identification.