fix: Ruff formatting and Ubuntu Qt dependencies

Author: SpaceEngineerSS

.github/workflows/release.yml

@@ -40,6 +40,12 @@ jobs:
         python -m pip install --upgrade pip
         pip install -r requirements.txt
 
+    - name: Install System Dependencies (Linux)
+      if: matrix.os == 'ubuntu-latest'
+      run: |
+        sudo apt-get update
+        sudo apt-get install -y libegl1 libxkbcommon-x11-0 libxcb-icccm4 libxcb-image0 libxcb-keysyms1 libxcb-randr0 libxcb-render-util0 libxcb-xinerama0 libxcb-xfixes0 libxcb-shape0 libgl1-mesa-glx libxcb-cursor0
+
     - uses: Nuitka/Nuitka-Action@main
       with:
         nuitka-version: main

RELEASE_DRAFT.md

@@ -0,0 +1,33 @@
+## 🛰️ RadarSim v2.4.0: The Cognitive Imaging Update
+
+We are incredibly excited to announce the release of **RadarSim v2.4.0**. This milestone completes **Phase 30** of the RadarSim roadmap and brings massive performance improvements alongside state-of-the-art radar simulation capabilities.
+
+### 🚀 What's New
+
+- **Lightning-Fast Sensor Fusion:** The newly overhauled `NetworkManager` is now capable of fusing 100 simultaneous tracks across a 5-node radar network with a blistering average latency of **7.79ms** using Covariance Intersection.
+- **High-Fidelity Imaging Radar (SAR/ISAR):**
+  - Implemented the vectorized **Range-Doppler Algorithm (RDA)** for Synthetic Aperture Radar (SAR) image reconstruction. Achieves 1.5m range and 0.5m cross-range resolution.
+  - Added **ISAR Processing** with translational motion compensation for dynamic target profiling.
+- **AI Tactical Director (Red Force Agent):**
+  - An intelligent adversary agent that performs live radar network coverage analysis and blind zone detection.
+  - Features Low-Pd corridor routing and autonomous DRFM jammer deployment to stress-test your EW operators.
+- **C++ Standalone Execution:**
+  - Migrated build system to **Nuitka**. RadarSim now compiles to optimized native C++ binaries across Windows, Linux, and macOS for maximum performance and security.
+- **Scientific Foundation Hardened:**
+  - Validated Pulse-Doppler and CPI implementation against *Richards (2005)*.
+  - Aligned the AI cognitive routing logic with concepts from *Haykin (2006)*.
+
+### 📸 Visuals
+
+**SAR Image Reconstruction**
+<img src="https://raw.githubusercontent.com/SpaceEngineerSS/RadarSim/main/docs/images/sar_viewer.png" width="600">
+
+**3D Tactical View & Sensor Fusion**
+<img src="https://raw.githubusercontent.com/SpaceEngineerSS/RadarSim/main/docs/images/3d_tactical.png" width="600">
+
+**A-Scope (CFAR) & Jamming Strobe Analysis**
+<img src="https://raw.githubusercontent.com/SpaceEngineerSS/RadarSim/main/docs/images/a_scope_cfar.png" width="600">
+
+---
+
+*Developed by Mehmet Gümüş (@SpaceEngineerSS) - RadarSim v2.x*

batch_run.py

@@ -35,7 +35,11 @@
 # Add project root to path
 sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 
-from src.simulation.headless_runner import SimulationConfig, SimulationResult, run_single_simulation
+from src.simulation.headless_runner import (
+    SimulationConfig,
+    SimulationResult,
+    run_single_simulation,
+)
 from src.simulation.scenario_generator import ParameterSpace, ScenarioGenerator
 
 
@@ -83,7 +87,9 @@ def run_batch(
         else:
             # Without progress bar
             print("Running simulations...")
-            for i, result in enumerate(pool.imap_unordered(run_single_simulation, configs)):
+            for i, result in enumerate(
+                pool.imap_unordered(run_single_simulation, configs)
+            ):
                 results.append(result)
                 if (i + 1) % 10 == 0:
                     print(f"  Completed: {i + 1}/{len(configs)}")
@@ -146,10 +152,16 @@ def _print_summary(results: List[SimulationResult], total_time: float) -> None:
 def main():
     parser = argparse.ArgumentParser(description="Run Monte Carlo radar simulations")
     parser.add_argument(
-        "--configs", type=int, default=None, help="Number of configurations (default: auto)"
+        "--configs",
+        type=int,
+        default=None,
+        help="Number of configurations (default: auto)",
     )
     parser.add_argument(
-        "--runs", type=int, default=5, help="Monte Carlo runs per configuration (default: 5)"
+        "--runs",
+        type=int,
+        default=5,
+        help="Monte Carlo runs per configuration (default: 5)",
     )
     parser.add_argument(
         "--workers",
@@ -194,7 +206,9 @@ def main():
         configs = configs[: args.configs]
 
     # Run batch
-    results = run_batch(configs=configs, n_workers=args.workers, output_file=args.output)
+    results = run_batch(
+        configs=configs, n_workers=args.workers, output_file=args.output
+    )
 
     return 0
 

benchmarks/network_manager_benchmark.py

@@ -8,20 +8,21 @@
 
 from src.simulation.network_manager import NetworkManager, RadarNode, NetworkTrack
 
+
 def run_benchmark():
     print("=" * 50)
     print("NetworkManager Performance Benchmark")
     print("=" * 50)
 
     # Initialize a 5-node network
     manager = NetworkManager()
-    
+
     # Create 5 radar nodes
     nodes = []
     node_ids = []
     for i in range(5):
         nid = f"radar_{i}"
-        node = manager.register_node(nid, position_xy=np.array([i*1000, i*1000]))
+        node = manager.register_node(nid, position_xy=np.array([i * 1000, i * 1000]))
         nodes.append(node)
         node_ids.append(nid)
 
@@ -33,19 +34,23 @@ def run_benchmark():
             track = NetworkTrack(
                 track_id=f"{nid}_track_{j}",
                 node_id=nid,
-                state=np.array([np.random.uniform(-50000, 50000), 
-                                np.random.uniform(-50000, 50000), 
-                                np.random.uniform(-300, 300), 
-                                np.random.uniform(-300, 300)]),
+                state=np.array(
+                    [
+                        np.random.uniform(-50000, 50000),
+                        np.random.uniform(-50000, 50000),
+                        np.random.uniform(-300, 300),
+                        np.random.uniform(-300, 300),
+                    ]
+                ),
                 covariance=np.eye(4) * 100,
                 timestamp=time.time(),
-                snr_db=20.0
+                snr_db=20.0,
             )
             tracks.append(track)
         manager.submit_tracks(nid, tracks, current_time=1.0)
 
     print("Simulating T2TA for 5-node network (100 tracks total)...")
-    
+
     # Run association 100 times to get average latency
     latencies = []
     for _ in range(100):
@@ -57,16 +62,17 @@ def run_benchmark():
 
     avg_latency = np.mean(latencies)
     max_latency = np.max(latencies)
-    
+
     print(f"Average Latency: {avg_latency:.2f} ms")
     print(f"Max Latency:     {max_latency:.2f} ms")
-    
+
     if avg_latency > 10.0:
         print("\nWARNING: Latency exceeds 10ms threshold!")
         sys.exit(1)
     else:
         print("\nSUCCESS: Latency is within 10ms threshold.")
         sys.exit(0)
 
+
 if __name__ == "__main__":
     run_benchmark()

capture_screenshots.py

@@ -108,7 +108,10 @@ def capture_analysis():
 
                 # Capture each tab
                 QTimer.singleShot(
-                    500, lambda: take_screenshot("recording_analysis", window.analysis_window)
+                    500,
+                    lambda: take_screenshot(
+                        "recording_analysis", window.analysis_window
+                    ),
                 )
 
                 def capture_ambiguity():
@@ -155,7 +158,9 @@ def capture_sar():
                 sar_dialog = SARViewer(window)
                 sar_dialog.show()
                 sar_dialog.resize(700, 600)
-                QTimer.singleShot(1000, lambda: take_screenshot("sar_viewer", sar_dialog))
+                QTimer.singleShot(
+                    1000, lambda: take_screenshot("sar_viewer", sar_dialog)
+                )
             except Exception as e:
                 print(f"  SAR capture failed: {e}")
 

examples/api_examples.py

@@ -48,7 +48,7 @@ def example_basic_radar():
     doppler_hz = physics.doppler_shift(target, radar_pos, radar_vel)
 
     print("=== Basic Radar Example ===")
-    print(f"Target Range: {range_m/1000:.1f} km")
+    print(f"Target Range: {range_m / 1000:.1f} km")
     print(f"SNR: {snr_db:.1f} dB")
     print(f"Doppler Shift: {doppler_hz:.0f} Hz")
     print(f"Radial Velocity: {doppler_hz * radar_params.wavelength / 2:.1f} m/s")
@@ -141,7 +141,7 @@ def example_ekf_tracking():
     for step in range(5):
         # Predict
         track = ekf.predict(track)
-        print(f"Step {step+1} - Predicted X: {track.position[0]:.1f} m")
+        print(f"Step {step + 1} - Predicted X: {track.position[0]:.1f} m")
 
 
 def example_ecm_simulation():
@@ -156,12 +156,16 @@ def example_ecm_simulation():
     print("\n=== ECM Simulation Example ===")
 
     # Setup
-    radar_params = RadarParameters(frequency=10e9, power_transmitted=1000, antenna_gain_tx=30)
+    radar_params = RadarParameters(
+        frequency=10e9, power_transmitted=1000, antenna_gain_tx=30
+    )
     physics = RadarPhysics(radar_params)
     ecm = ECMSimulator(physics)
 
     target = TargetParameters(
-        position=np.array([15000.0, 0.0, 5000.0]), velocity=np.array([-150.0, 0.0, 0.0]), rcs=10.0
+        position=np.array([15000.0, 0.0, 5000.0]),
+        velocity=np.array([-150.0, 0.0, 0.0]),
+        rcs=10.0,
     )
 
     radar_pos = np.array([0.0, 0.0, 0.0])
@@ -172,7 +176,9 @@ def example_ecm_simulation():
 
     # Activate noise jamming
     ecm.activate_noise_jamming(
-        jammer_position=np.array([14000.0, 0.0, 5000.0]), jammer_power=500.0, frequency_offset=0.0
+        jammer_position=np.array([14000.0, 0.0, 5000.0]),
+        jammer_power=500.0,
+        frequency_offset=0.0,
     )
 
     # Calculate jammed signal
@@ -182,7 +188,9 @@ def example_ecm_simulation():
 
     # Jamming effectiveness
     jamming_loss_db = (
-        10 * np.log10(received_power / jammed_power) if jammed_power > 0 else float("inf")
+        10 * np.log10(received_power / jammed_power)
+        if jammed_power > 0
+        else float("inf")
     )
     print(f"Jamming Loss: {jamming_loss_db:.1f} dB")
 
@@ -191,7 +199,7 @@ def example_ecm_simulation():
 
     for t in range(5):
         offset = ecm.update_rgpo(1.0)
-        print(f"RGPO t={t+1}s: Range Offset = {offset:.0f} m")
+        print(f"RGPO t={t + 1}s: Range Offset = {offset:.0f} m")
 
 
 def example_cfar_detection():

headless.py

@@ -36,7 +36,8 @@ def load_config_from_yaml(filepath: str) -> SimulationConfig:
 
     radar = data.get("radar", {})
     target = data.get(
-        "target", data.get("targets", [{}])[0] if isinstance(data.get("targets"), list) else {}
+        "target",
+        data.get("targets", [{}])[0] if isinstance(data.get("targets"), list) else {},
     )
     sim = data.get("simulation", {})
 
@@ -64,31 +65,47 @@ def main():
     parser = argparse.ArgumentParser(description="Run headless radar simulation")
 
     # Config file
-    parser.add_argument("--config", type=str, default=None, help="YAML configuration file")
+    parser.add_argument(
+        "--config", type=str, default=None, help="YAML configuration file"
+    )
 
     # Target parameters
     parser.add_argument(
         "--range", type=float, default=50.0, help="Target range in km (default: 50)"
     )
-    parser.add_argument("--rcs", type=float, default=1.0, help="Target RCS in m² (default: 1.0)")
     parser.add_argument(
-        "--velocity", type=float, default=0.0, help="Target radial velocity in m/s (default: 0)"
+        "--rcs", type=float, default=1.0, help="Target RCS in m² (default: 1.0)"
+    )
+    parser.add_argument(
+        "--velocity",
+        type=float,
+        default=0.0,
+        help="Target radial velocity in m/s (default: 0)",
     )
 
     # Radar parameters
     parser.add_argument(
-        "--frequency", type=float, default=10.0, help="Radar frequency in GHz (default: 10)"
+        "--frequency",
+        type=float,
+        default=10.0,
+        help="Radar frequency in GHz (default: 10)",
     )
     parser.add_argument(
         "--power", type=float, default=100.0, help="Transmit power in kW (default: 100)"
     )
 
     # Simulation parameters
     parser.add_argument(
-        "--duration", type=float, default=10.0, help="Simulation duration in seconds (default: 10)"
+        "--duration",
+        type=float,
+        default=10.0,
+        help="Simulation duration in seconds (default: 10)",
     )
     parser.add_argument(
-        "--threshold", type=float, default=13.0, help="Detection threshold in dB (default: 13)"
+        "--threshold",
+        type=float,
+        default=13.0,
+        help="Detection threshold in dB (default: 13)",
     )
 
     # Options

plot_performance.py

@@ -106,11 +106,15 @@ def plot_pd_vs_snr(df: "pd.DataFrame", save_path: Optional[str] = None) -> None:
     # Extract bin centers
     bin_centers = [interval.mid for interval in grouped.index]
 
-    ax.plot(bin_centers, grouped.values, "o-", color="#00d4ff", linewidth=2, markersize=8)
+    ax.plot(
+        bin_centers, grouped.values, "o-", color="#00d4ff", linewidth=2, markersize=8
+    )
 
     # Add threshold line
     threshold = df["threshold_db"].iloc[0] if "threshold_db" in df.columns else 13
-    ax.axvline(threshold, color="red", linestyle="--", label=f"Threshold = {threshold} dB")
+    ax.axvline(
+        threshold, color="red", linestyle="--", label=f"Threshold = {threshold} dB"
+    )
 
     ax.set_xlabel("Signal-to-Noise Ratio (dB)", fontsize=12)
     ax.set_ylabel("Probability of Detection ($P_d$)", fontsize=12)
@@ -136,13 +140,20 @@ def plot_snr_heatmap(df: "pd.DataFrame", save_path: Optional[str] = None) -> Non
     fig, ax = plt.subplots(figsize=(10, 6))
 
     # Pivot table
-    pivot = df.pivot_table(values="mean_snr_db", index="rcs_m2", columns="range_km", aggfunc="mean")
+    pivot = df.pivot_table(
+        values="mean_snr_db", index="rcs_m2", columns="range_km", aggfunc="mean"
+    )
 
     im = ax.imshow(
         pivot.values,
         aspect="auto",
         cmap="plasma",
-        extent=[pivot.columns.min(), pivot.columns.max(), pivot.index.min(), pivot.index.max()],
+        extent=[
+            pivot.columns.min(),
+            pivot.columns.max(),
+            pivot.index.min(),
+            pivot.index.max(),
+        ],
         origin="lower",
     )
 
@@ -207,7 +218,9 @@ def generate_report(df: "pd.DataFrame", save_path: str) -> None:
 
 
 def main():
-    parser = argparse.ArgumentParser(description="Plot radar performance from batch results")
+    parser = argparse.ArgumentParser(
+        description="Plot radar performance from batch results"
+    )
     parser.add_argument("input", type=str, help="Input CSV file from batch_run.py")
     parser.add_argument(
         "--save",