Radar Imaging Toolbox (TDBP, RDA, Analysis)

This repository is a modular, high‑performance Python toolbox for Synthetic Aperture Radar (SAR) imaging.

It extends the MATLAB-based project Time-Domain-Back-Projection-Method by mrzamaniiii, adding:

• Multiple imaging backends (Python, Numba, GPU stub, RDA)
• High‑performance parallel kernels (~9× speedup with Numba)
• Frequency‑domain Range‑Doppler Algorithm (RDA)
• Full test suite (58 tests) + benchmarks
• Deterministic synthetic fixtures
• Modular SAR analysis utilities
• Command-Line Interface & Module Execution

This is now a complete SAR imaging library, not just a port.

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Project Structure

File / Path	Description
dataset_UAV.mat	Radar dataset: UAV trajectory, range axis, raw radar returns.
src/sar_toolbox/__main__.py	CLI entry point for package execution and quick benchmarking.
src/sar_toolbox/tdbp.py	Time‑Domain Back‑Projection with multi‑backend support (python, numba, gpu, rda).
src/sar_toolbox/rda.py	Range‑Doppler Algorithm (RDA) — FFT‑based SAR imaging backend.
src/sar_toolbox/despeckling.py	Speckle reduction filters (3×3, 5×5, 7×7).
src/sar_toolbox/corner_reflectors.py	Corner reflector analysis: azimuth & range resolution.
src/sar_toolbox/analysis.py	Bandwidth estimation, trajectory error simulation, frequency analysis.
tests/	Full pytest suite (backend parity, interpolation, CLI, phase correctness, determinism, benchmarks).
Codes/	Original MATLAB reference scripts.

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Key Features

• Multi‑backend TDBP

• Pure Python reference implementation

• Numba‑accelerated parallel kernel (~9× faster)

• GPU backend placeholder (CuPy-ready)

• RDA backend for FFT-based imaging

• Range-Doppler Algorithm (RDA)

• Frequency-domain SAR imaging

• Extremely fast (110–250 µs for typical grids)

• Perfect for benchmarking and comparison

• SAR Analysis Tools

• Bandwidth estimation

• Range resolution

• Azimuth resolution

• Corner reflector patch analysis

• Trajectory error simulation

• Image Processing & CLI

• Speckle reduction filters

• FFT-based diagnostics

• Direct terminal execution via sar-toolbox or python -m sar_toolbox

• Engineering Practices

• 58 tests

• 77% coverage

• Benchmarks (pytest‑benchmark)

• CI-ready with matrix fallback validation

• Deterministic synthetic fixtures

• Modular architecture

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How to Run

1. Install in editable mode with test dependencies:

pip install -e .[test]

2. Generate the synthetic test dataset:

python scripts/generate_data.py

3. Run the full test suite:

pytest

4. Run benchmarks:

pytest tests/benchmarks -q

5. Use the Command-Line Interface:

sar-toolbox --test-mode --backend numba --plot

Alternatively, execute via module syntax:

python -m sar_toolbox --test-mode --backend python

6. Or use programmatically:

from sar_toolbox.tdbp import run_tdbp

result = run_tdbp("dataset_UAV.mat", backend="numba")

Backends available:

python, numba, gpu, rda