improved readme

README.md

@@ -1,29 +1,121 @@
-# radar_system
+# Radar System
 
-Radar acquisition and processing system for single LibreVNA, synchronized
-multi-device LibreVNA, Compact-M K209/S2VNA, and Kamil ADC setups.
+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.
 
-Start here:
+## Overview
 
-- [Operation Modes](docs/operation_modes.md): what to run on each machine for
-  `librevna`, `librevna_multi`, `compact_m_k209`, and `kamil_adc`.
-- [Run Config Reference](docs/run_config.md): `run_config.json` fields and
-  example files.
-- [K209 Setup](docs/k209_setup.md): S2VNA, VISA, K209 limits, smoke tests, and
-  Raspberry Pi remote mode details.
+The system is split into a **C++ real-time pipeline** and a **Python application**
+that supervises it and handles visualization and control.
 
-Common local setup:
+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
-cd /path/to/radar_system
 python3 -m venv .venv
 .venv/bin/python -m pip install --upgrade pip
 .venv/bin/python -m pip install -r requirements.txt
-make
+make                     # build the C++ binaries into build/bin/
 ```
 
-Run the GUI:
+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
 ```