# 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, or GPR images with detected
   objects). 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).

## 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).
- A C++20 compiler and `make` — Eigen and nlohmann/json are vendored, so no extra C++ dependencies are needed.
- Python 3.12 with a virtual environment.

## Build and run

From the repository root:

```bash
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/
```

Run the desktop GUI:

```bash
.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),
`--kamil-adc`, `--profile PATH`, `--producer-only`, `--skip-build`, and
`--clean-shm`; see `./start.sh --help`.

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

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