123 lines
5.4 KiB
Markdown
123 lines
5.4 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, or GPR images with detected
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objects). Detected objects are additionally broadcast to subscribers over a TCP
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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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## 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).
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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 3.12 with a virtual environment.
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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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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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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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