radar_system/README.md

Radar System

A real-time radar acquisition and imaging system for vector-network-analyzer (VNA) and ADC front-ends. It drives the radar through a frequency sweep, calibrates and preprocesses the measured S-parameters, runs a selectable imaging/detection processor, and presents the live result in a desktop GUI and an in-process web interface. The same application runs interactively on a workstation or unattended as a headless Raspberry Pi appliance.

Overview

The system is split into a C++ real-time pipeline and a Python application that supervises it and handles visualization and control.

The pipeline runs as three independent processes connected by lock-free POSIX shared-memory ring buffers. Each stage advances on its own, and a slow consumer never blocks a producer — the rings are latest-wins, so a reader that falls behind simply skips to the freshest frame:

1. Acquisition — sweep_orchestrator (or a hardware-specific producer) configures the radar, performs the frequency sweep, and publishes raw S11/S21 traces.
2. Preprocessing — data_preprocessor applies through/OSL calibration, reference subtraction, and optional frequency-domain notch filtering.
3. Processing — data_processor runs the selected processing mode and emits results (pass-through traces, B-scan images, GPR images with detected objects, or 3D SAR views). Detected objects are additionally broadcast to subscribers over a TCP locator server.

The Python application (python_app) starts and monitors the C++ binaries, reads the result rings, renders the live view with pyqtgraph, and exposes the same controls plus a pixel-identical live plot through an embedded web UI.

 radar hardware
       │
       ▼
 Acquisition ──[raw ring]──▶ Preprocessing ──[preprocessed ring]──▶ Processing ──[result ring]──▶ GUI / Web
                                                                          │
                                                                          └──▶ TCP locator (detected objects)

Supported hardware

• librevna — single LibreVNA.
• librevna_multi — synchronized multi-device LibreVNA.
• compact_m_k209 — Compact-M K209 / S2VNA (including Raspberry Pi remote mode).
• sn9000 — SNVNA SN9000 over VISA (pure-Python @py backend).
• kamil_adc — external Kamil ADC acquisition process, with optional laser control.
• mock / simulator — driver-less mode for development without hardware.

Processing modes

• pass_through — magnitude and phase of the selected channel for each combo.
• bscan — depth-versus-sweep B-scan heatmap.
• gpr — coherent back-projection GPR imaging with object detection.
• legacy_gpr — ellipse-intersection MIMO localization (point and extended/region modes).
• sar3d — 3D SAR of the reference notebook Multi_antenna_3D_clean_v1.ipynb on an NVIDIA GPU: front (X-Y), side (Z-Y), top (X-Z) and 3D views of the imaged volume. Its settings carry the notebook's parameter names; the scene window is fixed (X −2…2 m, height −0.5…2 m above the ground), its range extent follows MIN_DEPTH/MAX_DEPTH, and the voxels are cubes with edge SAR_VOXEL_M (default 2 cm). The radar motion is corrected from each trace's capture timestamps, so the sweep timing needs no parameters.

Repository layout

Path	Contents
data_acq_and_processing/	C++ pipeline: sweep_orchestrator/, preprocessing/, processing/ (including locator/), shared common_cpp/ (config, IPC rings), and vendored third_party/ (Eigen, nlohmann/json).
python_app/	Python application: gui/, orchestration/ (process supervisor, shared-memory readers, config), hardware_full/ (device drivers and services), webui/, storage/, models/, workflows/, scripts/ (producers and tools), and tests/.
run_config_examples/	Ready-made configuration profiles, one per hardware setup.
deploy/	systemd daemon installer for Raspberry Pi appliances.
build/bin/	Compiled C++ binaries.
device_firmware/, docs/	Device firmware and vendor manuals.

Requirements

• Linux (POSIX shared memory; GPIO on Raspberry Pi). The deployment target is a Raspberry Pi 5; hardware PWM gating for the generator sweep is Pi 5 specific (GPIO12/13/18/19, dtoverlay=pwm-2chan).
• A C++20 compiler and make — Eigen and nlohmann/json are vendored, so no extra C++ dependencies are needed.
• Python 3.10 or newer, in a virtual environment.
• For the sar3d processor: an NVIDIA GPU with its driver (libcuda). The kernels are compiled at run time by NVRTC, so there is no nvcc step; make needs only nvrtc.h, cuda.h and libnvrtc, taken from the pip nvidia-cuda-* packages in .venv (requirements.txt, x86_64; CUDA 13 covers Turing and newer GPUs) or from a system toolkit in /usr/local/cuda (Jetson). Without them everything else still builds, and sar3d reports that CUDA is missing — a Raspberry Pi needs none of it.

Build and run

From the repository root:

python3 -m venv .venv
.venv/bin/python -m pip install --upgrade pip
.venv/bin/python -m pip install -r requirements.txt
make                     # build the C++ binaries into build/bin/

make builds the sar3d GPU code when it finds CUDA (see Requirements) and prints a note when it does not. After installing the CUDA requirements into an existing venv, run make again: it notices the change and rebuilds that part.

Run the desktop GUI:

.venv/bin/python -m python_app.gui.main
# or, with the build step and single-instance handling:
./start.sh

start.sh supports --headless (offscreen, auto-start — for unattended Pi use), --profile PATH, --producer-only, --skip-build, and --clean-shm; see ./start.sh --help. The acquisition device (LibreVNA, Kamil ADC, …) is detected automatically from the active config's radar.model — there is no per-device flag.

Jetson (aarch64) bring-up

Ubuntu 22.04 on Jetson has glibc 2.35, and PyQt6 6.8+ ships only manylinux_2_39 aarch64 wheels — requirements.txt therefore pins PyQt6 to <6.8 on aarch64 (6.7.1 is the last release with a compatible wheel). With that pin the stock ./start.sh provisioning works; the extra steps are only for the CUDA-backed sar3d processor:

# 1. copy the replay dataset (for dataset-replay profiles) and point
#    radar.replay.dataset_dir in the profile at the local copy — the path in
#    run_config_examples/*.json is machine-specific.

# 2. normal launch; make takes the JetPack CUDA from /usr/local/cuda
#    (requirements.txt installs no pip nvidia-* packages on aarch64) and the
#    driver's /usr/lib/aarch64-linux-gnu/nvidia/libcuda.so.1:
./start.sh --profile run_config_examples/run_config_replay_vna.example.json

Running without hardware (emulation)

Two ways to run the full pipeline with no radar attached:

Mock producer (synthetic data). The default run_config.json already sets radar.driver_mode: "mock", so the producer generates synthetic S-parameters. The preprocessor still requires S21 calibration/reference sets; create them once:

.venv/bin/python -m python_app.scripts.manual_smoke_run --duration 1.0  # creates smoke_cal/smoke_ref
./start.sh   # then in the GUI: Preprocessing Panel -> pick the sets -> Start

(Alternatively use the GUI's "Create neutral sets" button, which makes identity calibration + zero reference so preprocessing is a pass-through.)

Dataset replay (recorded scene, meaningful sar3d picture). When radar.replay.dataset_dir is set in the profile, the supervisor launches python_app.scripts.replay_raw_producer instead of the normal producer: it replays recorded snapshots into the raw ring at the recorded cadence, looping forever with a monotonic timeline. Pair it with NEUTRAL preprocess sets so the already-preprocessed recording passes through the preprocessor bit-exactly. A ready profile for the VNA_AlGo tank005 dataset is provided:

# one-time: neutral sets for this profile (or use the GUI button)
.venv/bin/python -m python_app.scripts.create_neutral_preprocess_sets \
    --config run_config_examples/run_config_replay_vna.example.json
# interactive GUI (mode sar3d is pre-selected by the profile's gui section):
./start.sh --profile run_config_examples/run_config_replay_vna.example.json

The sar3d processor keeps about 75 MB of buffers on the GPU at the default 2 cm voxel and depth 2…8 m (plus the CUDA context). The volume grows as 1/SAR_VOXEL_M³: 1 cm takes about 600 MB and ran about 6 times slower in testing. If the GPU has no room, it logs the CUDA error once and publishes empty results until memory frees.

The web interface is served in-process at http://<host>:8080. It mirrors the live plot and offers Start / Stop / Capture controls and the active processor settings.

Configuration

The active profile is run_config.json. It selects the radar model and sweep, the switch matrix and acquisition combos, the preprocessing calibration/reference assets, the GPR geometry, and the shared-memory ring sizes. Profiles for each hardware setup are provided in run_config_examples/ — copy one to run_config.json, or pass it with --profile, to use it.

Deployment

On a Raspberry Pi, deploy/install-daemon.sh installs a systemd service that runs the application headless and restarts it on failure. A single-instance lock ensures the interactive GUI and the daemon never run at the same time, since they share the radar, the shared-memory rings, and the locator port.

Tests

.venv/bin/python -m unittest discover -s python_app/tests