Ayzen
45861f4047
The sar3d processor is rewritten as a checked port of Multi_antenna_3D_clean_v1.ipynb: NVRTC kernels, timing from the trace stamps, notebook parameter names and one cubic SAR_VOXEL_M. It adds front, side, top and 3D views, drops gpr_cpp and the parity tools, and the GUI speed now applies only while the socket speed is ignored.
200 lines
9.6 KiB
Markdown
200 lines
9.6 KiB
Markdown
# Radar System
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A real-time radar acquisition and imaging system for vector-network-analyzer (VNA)
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and ADC front-ends. It drives the radar through a frequency sweep, calibrates and
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preprocesses the measured S-parameters, runs a selectable imaging/detection
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processor, and presents the live result in a desktop GUI and an in-process web
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interface. The same application runs interactively on a workstation or unattended
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as a headless Raspberry Pi appliance.
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## Overview
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The system is split into a **C++ real-time pipeline** and a **Python application**
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that supervises it and handles visualization and control.
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The pipeline runs as three independent processes connected by lock-free POSIX
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shared-memory ring buffers. Each stage advances on its own, and a slow consumer
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never blocks a producer — the rings are latest-wins, so a reader that falls behind
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simply skips to the freshest frame:
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1. **Acquisition** — `sweep_orchestrator` (or a hardware-specific producer)
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configures the radar, performs the frequency sweep, and publishes raw S11/S21
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traces.
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2. **Preprocessing** — `data_preprocessor` applies through/OSL calibration,
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reference subtraction, and optional frequency-domain notch filtering.
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3. **Processing** — `data_processor` runs the selected processing mode and emits
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results (pass-through traces, B-scan images, GPR images with detected objects,
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or 3D SAR views). Detected objects are additionally broadcast to subscribers
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over a TCP locator server.
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The **Python application** (`python_app`) starts and monitors the C++ binaries,
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reads the result rings, renders the live view with pyqtgraph, and exposes the same
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controls plus a pixel-identical live plot through an embedded web UI.
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```
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radar hardware
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│
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▼
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Acquisition ──[raw ring]──▶ Preprocessing ──[preprocessed ring]──▶ Processing ──[result ring]──▶ GUI / Web
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│
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└──▶ TCP locator (detected objects)
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```
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## Supported hardware
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- **librevna** — single LibreVNA.
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- **librevna_multi** — synchronized multi-device LibreVNA.
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- **compact_m_k209** — Compact-M K209 / S2VNA (including Raspberry Pi remote mode).
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- **sn9000** — SNVNA SN9000 over VISA (pure-Python `@py` backend).
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- **kamil_adc** — external Kamil ADC acquisition process, with optional laser control.
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- **mock / simulator** — driver-less mode for development without hardware.
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## Processing modes
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- **pass_through** — magnitude and phase of the selected channel for each combo.
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- **bscan** — depth-versus-sweep B-scan heatmap.
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- **gpr** — coherent back-projection GPR imaging with object detection.
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- **legacy_gpr** — ellipse-intersection MIMO localization (point and extended/region modes).
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- **sar3d** — 3D SAR of the reference notebook `Multi_antenna_3D_clean_v1.ipynb`
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on an NVIDIA GPU: front (X-Y), side (Z-Y), top (X-Z) and 3D views of the imaged
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volume. Its settings carry the notebook's parameter names; the scene window is
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fixed (X −2…2 m, height −0.5…2 m above the ground), its range extent follows
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`MIN_DEPTH`/`MAX_DEPTH`, and the voxels are cubes with edge `SAR_VOXEL_M`
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(default 2 cm). The radar motion is corrected from each trace's capture
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timestamps, so the sweep timing needs no parameters.
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## Repository layout
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| Path | Contents |
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| --- | --- |
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| `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). |
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| `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/`. |
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| `run_config_examples/` | Ready-made configuration profiles, one per hardware setup. |
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| `deploy/` | systemd daemon installer for Raspberry Pi appliances. |
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| `build/bin/` | Compiled C++ binaries. |
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| `device_firmware/`, `docs/` | Device firmware and vendor manuals. |
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## Requirements
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- Linux (POSIX shared memory; GPIO on Raspberry Pi). The deployment target is a
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**Raspberry Pi 5**; hardware PWM gating for the generator sweep is Pi 5
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specific (GPIO12/13/18/19, `dtoverlay=pwm-2chan`).
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- A C++20 compiler and `make` — Eigen and nlohmann/json are vendored, so no extra C++ dependencies are needed.
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- **Python 3.10 or newer**, in a virtual environment.
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- For the `sar3d` processor: an NVIDIA GPU with its driver (`libcuda`). The
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kernels are compiled at run time by NVRTC, so there is no nvcc step; `make`
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needs only `nvrtc.h`, `cuda.h` and `libnvrtc`, taken from the pip
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`nvidia-cuda-*` packages in `.venv` (`requirements.txt`, x86_64; CUDA 13 covers
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Turing and newer GPUs) or from a system toolkit in `/usr/local/cuda` (Jetson).
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Without them everything else still builds, and sar3d reports that CUDA is
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missing — a Raspberry Pi needs none of it.
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## Build and run
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From the repository root:
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```bash
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python3 -m venv .venv
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.venv/bin/python -m pip install --upgrade pip
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.venv/bin/python -m pip install -r requirements.txt
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make # build the C++ binaries into build/bin/
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```
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`make` builds the sar3d GPU code when it finds CUDA (see Requirements) and
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prints a note when it does not. After installing the CUDA requirements into an
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existing venv, run `make` again: it notices the change and rebuilds that part.
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Run the desktop GUI:
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```bash
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.venv/bin/python -m python_app.gui.main
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# or, with the build step and single-instance handling:
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./start.sh
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```
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`start.sh` supports `--headless` (offscreen, auto-start — for unattended Pi use),
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`--profile PATH`, `--producer-only`, `--skip-build`, and `--clean-shm`; see
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`./start.sh --help`. The acquisition device (LibreVNA, Kamil ADC, …) is detected
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automatically from the active config's `radar.model` — there is no per-device flag.
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## Jetson (aarch64) bring-up
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Ubuntu 22.04 on Jetson has glibc 2.35, and PyQt6 6.8+ ships only
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`manylinux_2_39` aarch64 wheels — `requirements.txt` therefore pins PyQt6 to
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`<6.8` on aarch64 (6.7.1 is the last release with a compatible wheel). With
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that pin the stock `./start.sh` provisioning works; the extra steps are only
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for the CUDA-backed `sar3d` processor:
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```bash
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# 1. copy the replay dataset (for dataset-replay profiles) and point
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# radar.replay.dataset_dir in the profile at the local copy — the path in
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# run_config_examples/*.json is machine-specific.
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# 2. normal launch; make takes the JetPack CUDA from /usr/local/cuda
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# (requirements.txt installs no pip nvidia-* packages on aarch64) and the
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# driver's /usr/lib/aarch64-linux-gnu/nvidia/libcuda.so.1:
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./start.sh --profile run_config_examples/run_config_replay_vna.example.json
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```
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## Running without hardware (emulation)
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Two ways to run the full pipeline with no radar attached:
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**Mock producer (synthetic data).** The default `run_config.json` already sets
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`radar.driver_mode: "mock"`, so the producer generates synthetic S-parameters.
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The preprocessor still requires S21 calibration/reference sets; create them once:
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```bash
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.venv/bin/python -m python_app.scripts.manual_smoke_run --duration 1.0 # creates smoke_cal/smoke_ref
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./start.sh # then in the GUI: Preprocessing Panel -> pick the sets -> Start
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```
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(Alternatively use the GUI's "Create neutral sets" button, which makes
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identity calibration + zero reference so preprocessing is a pass-through.)
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**Dataset replay (recorded scene, meaningful sar3d picture).** When
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`radar.replay.dataset_dir` is set in the profile, the supervisor launches
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`python_app.scripts.replay_raw_producer` instead of the normal producer: it
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replays recorded snapshots into the raw ring at the recorded cadence, looping
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forever with a monotonic timeline. Pair it with NEUTRAL preprocess sets so the
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already-preprocessed recording passes through the preprocessor bit-exactly.
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A ready profile for the VNA_AlGo tank005 dataset is provided:
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```bash
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# one-time: neutral sets for this profile (or use the GUI button)
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.venv/bin/python -m python_app.scripts.create_neutral_preprocess_sets \
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--config run_config_examples/run_config_replay_vna.example.json
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# interactive GUI (mode sar3d is pre-selected by the profile's gui section):
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./start.sh --profile run_config_examples/run_config_replay_vna.example.json
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```
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The sar3d processor keeps about 75 MB of buffers on the GPU at the default
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2 cm voxel and depth 2…8 m (plus the CUDA context). The volume grows as
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1/`SAR_VOXEL_M`³: 1 cm takes about 600 MB and ran about 6 times slower in
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testing. If the GPU has no room, it logs the CUDA error once and publishes empty
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results until memory frees.
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The web interface is served in-process at `http://<host>:8080`. It mirrors the live
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plot and offers Start / Stop / Capture controls and the active processor settings.
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## Configuration
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The active profile is `run_config.json`. It selects the radar model and sweep, the
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switch matrix and acquisition combos, the preprocessing calibration/reference
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assets, the GPR geometry, and the shared-memory ring sizes. Profiles for each
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hardware setup are provided in `run_config_examples/` — copy one to
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`run_config.json`, or pass it with `--profile`, to use it.
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## Deployment
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On a Raspberry Pi, `deploy/install-daemon.sh` installs a systemd service that runs
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the application headless and restarts it on failure. A single-instance lock ensures
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the interactive GUI and the daemon never run at the same time, since they share the
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radar, the shared-memory rings, and the locator port.
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## Tests
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```bash
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.venv/bin/python -m unittest discover -s python_app/tests
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```
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