Create radar_ai.yaml

Signed-off-by: Gilbert Algordo <69397216+gilbertalgordo@users.noreply.github.com>

Author: gilbertalgordo

radar_ai.yaml

@@ -0,0 +1,174 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+class RadarInstance:
+    def __init__(self, range_max=100, fov=90):
+        self.range_max = range_max
+        self.fov = fov  # Field of View in degrees
+        self.targets = []
+
+    def add_target(self, distance, angle, velocity, rcs):
+        """
+        RCS = Radar Cross Section (how 'visible' the object is)
+        """
+        self.targets.append({
+            'dist': distance, 
+            'angle': np.radians(angle), 
+            'vel': velocity, 
+            'rcs': rcs
+        })
+
+    def simulate_scan(self, noise_level=0.1):
+        """Simulates raw sensor data with Gaussian noise."""
+        detections = []
+        for t in self.targets:
+            # Signal Strength calculation based on Inverse Square Law
+            signal_strength = t['rcs'] / (t['dist']**2)
+            if signal_strength > noise_level:
+                # Add synthetic noise to the reading
+                detected_dist = t['dist'] + np.random.normal(0, 0.5)
+                detections.append([detected_dist, t['angle'], t['vel']])
+        return np.array(detections)
+
+# --- Instance Initialization ---
+radar_unit = RadarInstance()
+radar_unit.add_target(distance=65, angle=30, velocity=250, rcs=1.5) # Fast Jet
+radar_unit.add_target(distance=30, angle=-15, velocity=15, rcs=0.5) # Drone
+
+
+
+def classify_target(velocity, rcs):
+    """Simple AI heuristic for target identification."""
+    if velocity > 200:
+        return "STRIKE_AIRCRAFT", "HIGH"
+    elif velocity > 50 and rcs < 0.2:
+        return "STEALTH_UAV", "CRITICAL"
+    elif velocity < 20:
+        return "STATIC_OBSTACLE", "LOW"
+    else:
+        return "UNKNOWN", "MEDIUM"
+
+
+
+def render_hud(detections):
+    fig = plt.figure(figsize=(8, 8), facecolor='black')
+    ax = fig.add_subplot(111, polar=True)
+    ax.set_facecolor('#001100')
+    
+    # HUD Styling
+    ax.tick_params(colors='lime')
+    ax.grid(color='lime', alpha=0.3)
+    
+    if len(detections) > 0:
+        dist = detections[:, 0]
+        angles = detections[:, 1]
+        vels = detections[:, 2]
+        
+        for i in range(len(dist)):
+            label, threat = classify_target(vels[i], 1.0)
+            ax.scatter(angles[i], dist[i], color='red', s=100, marker='x')
+            ax.text(angles[i], dist[i]+5, f"{label}\n{vels[i]}m/s", 
+                    color='lime', fontsize=9, fontweight='bold')
+
+    ax.set_ylim(0, 100)
+    plt.title("AI RADAR INSTANCE v1.0 - ACTIVE SCAN", color='lime', pad=20)
+    plt.show()
+
+# Execute Scan
+data = radar_unit.simulate_scan()
+render_hud(data)
+
+
+
+import numpy as np
+from scipy import signal
+
+def get_micro_doppler_signature(raw_signal, fs=1000):
+    """
+    Converts 1D Radar signal into a 2D Time-Frequency Spectrogram.
+    This 'image' is what the AI actually 'sees'.
+    """
+    frequencies, times, spectrogram = signal.spectrogram(
+        raw_signal, fs, nperseg=64, noverlap=32, return_onesided=True
+    )
+    # Log scale for better AI feature extraction
+    return 10 * np.log10(spectrogram + 1e-10)
+
+
+
+
+from tensorflow.keras import layers, models
+
+def build_advanced_radar_ai():
+    model = models.Sequential([
+        # Input: (Frames, Height, Width, Channels)
+        layers.Input(shape=(10, 64, 64, 1)), 
+        
+        # Spatial-Temporal Feature Extraction
+        layers.ConvLSTM2D(32, (3, 3), padding='same', return_sequences=False, activation='relu'),
+        layers.BatchNormalization(),
+        
+        layers.Flatten(),
+        layers.Dense(128, activation='relu'),
+        layers.Dropout(0.3),
+        layers.Dense(4, activation='softmax') # [DRONE, BIRD, PERSON, VEHICLE]
+    ])
+    model.compile(optimizer='adam', loss='categorical_crossentropy')
+    return model
+
+radar_classifier = build_advanced_radar_ai()
+
+
+
+class KalmanTracker:
+    def __init__(self, dt=0.1):
+        # State: [pos_x, pos_y, vel_x, vel_y]
+        self.x = np.zeros((4, 1))
+        # Transition Matrix
+        self.F = np.array([[1, 0, dt, 0],
+                           [0, 1, 0, dt],
+                           [0, 0, 1, 0],
+                           [0, 0, 0, 1]])
+        # Measurement Matrix (we only measure position)
+        self.H = np.array([[1, 0, 0, 0],
+                           [0, 1, 0, 0]])
+        self.P = np.eye(4) * 100 # Uncertainty
+        self.R = np.eye(2) * 1   # Measurement Noise
+
+    def predict(self):
+        self.x = np.dot(self.F, self.x)
+        self.P = np.dot(np.dot(self.F, self.P), self.F.T)
+        return self.x
+
+    def update(self, z):
+        # Kalman Gain
+        S = np.dot(self.H, np.dot(self.P, self.H.T)) + self.R
+        K = np.dot(np.dot(self.P, self.H.T), np.linalg.inv(S))
+        # Update State
+        y = z - np.dot(self.H, self.x)
+        self.x = self.x + np.dot(K, y)
+        self.P = self.P - np.dot(np.dot(K, self.H), self.P)
+
+
+
+# Initialization
+tracker = KalmanTracker()
+# ... (simulate incoming radar data stream) ...
+
+def process_frame(raw_iq_data):
+    # 1. AI Classification
+    spec = get_micro_doppler_signature(raw_iq_data)
+    # prediction = radar_classifier.predict(spec) 
+    
+    # 2. State Estimation (Tracking)
+    # Assume z is [detected_x, detected_y]
+    z = np.array([[50.5], [20.2]]) 
+    tracker.update(z)
+    future_state = tracker.predict()
+    
+    return {
+        "id": "TGT-01",
+        "class": "STRIKE_UAV",
+        "pos": (future_state[0,0], future_state[1,0]),
+        "vel": (future_state[2,0], future_state[3,0])
+    }