215 lines
8.1 KiB
Python
215 lines
8.1 KiB
Python
#!/usr/bin/env python3
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"""
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Interceptor Drone Swarm vs Shahed Missile Simulation
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=====================================================
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Simulates 3 pre-positioned interceptor drones that fly in the same
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direction as the Shahed missile, using IR tracking to home in and collide.
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Key concept: Lower delta speed gives more time for AI adjustments.
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3-drone formation absorbs radar positioning errors.
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"""
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import numpy as np
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# =============================================================================
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# Constants
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# =============================================================================
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SHAHED_SPEED = 136.0 # m/s (jet-powered Shahed)
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INTERCEPTOR_SPEED = 100.0 # m/s (slower, but same direction)
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IR_TRACKING_RANGE = 5000.0 # 5 km
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IR_HOMING_ACCURACY = 5.0 # degrees
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RADAR_POSITION_ERROR = 20.0 # meters (mean)
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DRONE_FORMATION_SPACING = 50.0 # meters between drones
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# Simulation parameters
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DT = 0.1 # 100 ms time step
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SIM_DURATION = 300.0 # 5 minutes
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# =============================================================================
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# Scenario Setup
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# =============================================================================
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# Shahed trajectory: approaching city from north
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SHAHED_START = np.array([0.0, 40000.0, 2000.0]) # 40 km north, 2 km altitude
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SHAHED_VELOCITY = np.array([0.0, -SHAHED_SPEED, 0.0]) # Moving south
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# Interceptor drone launch positions (pre-positioned around terrain)
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# Positioned to intercept Shahed at ~10 km from city
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INTERCEPTOR_LAUNCH_POS = np.array([0.0, 15000.0, 1500.0])
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# Radar-detected trajectory (with error)
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def get_radar_trajectory():
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"""Simulate radar-detected trajectory with positioning error."""
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error = np.random.normal(0, RADAR_POSITION_ERROR, 3)
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detected_start = SHAHED_START + error
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return detected_start, SHAHED_VELOCITY
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# =============================================================================
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# Simulation Functions
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# =============================================================================
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def shahed_position(t):
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"""True Shahed position at time t."""
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return SHAHED_START + SHAHED_VELOCITY * t
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def drone_position(t, launch_time, velocity):
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"""Interceptor drone position at time t."""
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if t < launch_time:
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return INTERCEPTOR_LAUNCH_POS
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return INTERCEPTOR_LAUNCH_POS + velocity * (t - launch_time)
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def ir_tracking_error(distance):
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"""IR tracking error decreases with closer distance."""
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base_error = IR_HOMING_ACCURACY
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if distance < 1000:
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return base_error * 0.5
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elif distance < 2000:
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return base_error * 0.75
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return base_error
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def simulate_interception():
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"""Simulate the interception scenario."""
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print("=" * 80)
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print("Interceptor Drone Swarm vs Shahed Missile Simulation")
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print("=" * 80)
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print(f"Shahed speed: {SHAHED_SPEED} m/s")
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print(f"Interceptor speed: {INTERCEPTOR_SPEED} m/s")
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print(f"IR tracking range: {IR_TRACKING_RANGE/1000:.0f} km")
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print(f"Radar position error: {RADAR_POSITION_ERROR} m")
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print()
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# Get radar-detected trajectory
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detected_start, detected_velocity = get_radar_trajectory()
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radar_error = np.linalg.norm(detected_start - SHAHED_START)
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print(f"Radar detected Shahed with {radar_error:.1f} m position error")
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print()
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# Calculate interception point based on radar data
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# Drones fly to intercept at ~10 km from city
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intercept_distance = 10000.0 # 10 km from city center
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intercept_time = (40000.0 - intercept_distance) / SHAHED_SPEED
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intercept_point = SHAHED_START + SHAHED_VELOCITY * intercept_time
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print(f"Predicted interception point: {intercept_point[1]/1000:.0f} km from city")
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print(f"Predicted interception time: {intercept_time:.0f} s")
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print()
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# Launch 3 interceptor drones in formation
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# Middle drone is the "killer", flank drones provide IR tracking
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drone_positions = [
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INTERCEPTOR_LAUNCH_POS + np.array([DRONE_FORMATION_SPACING, 0, 0]), # Left
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INTERCEPTOR_LAUNCH_POS, # Middle (killer)
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INTERCEPTOR_LAUNCH_POS + np.array([-DRONE_FORMATION_SPACING, 0, 0]), # Right
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]
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# Calculate drone velocity to reach interception point
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drone_travel_time = intercept_time - 30.0 # 30s reaction time
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if drone_travel_time <= 0:
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print("ERROR: Not enough time for drones to reach interception point")
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return
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drone_velocity = (intercept_point - INTERCEPTOR_LAUNCH_POS) / drone_travel_time
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drone_speed = np.linalg.norm(drone_velocity)
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print(f"Drone velocity: {drone_speed:.1f} m/s")
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print()
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# Simulate the interception
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print("Simulation:")
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print("-" * 80)
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t = 0.0
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while t < SIM_DURATION:
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shahed_pos = shahed_position(t)
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# Check if Shahed is within IR tracking range of drones
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drone_pos = drone_positions[1] # Middle drone
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if t > 30.0: # After launch
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drone_pos = INTERCEPTOR_LAUNCH_POS + drone_velocity * (t - 30.0)
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distance = np.linalg.norm(shahed_pos - drone_pos)
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if distance < IR_TRACKING_RANGE:
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# IR tracking active
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tracking_error = ir_tracking_error(distance)
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# Drones adjust based on IR tracking
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# Simulate homing correction
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correction_factor = 1.0 - (distance / IR_TRACKING_RANGE)
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actual_drone_pos = drone_pos + (shahed_pos - drone_pos) * correction_factor * 0.1
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# Check for collision (middle drone)
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if distance < 10.0: # 10 m collision radius
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print(f"t={t:6.1f}s | COLLISION! Distance: {distance:.1f} m")
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print(f"Shahed destroyed at {shahed_pos[1]/1000:.0f} km from city")
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return True
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# Print status every 10 seconds
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if int(t) % 10 == 0 and t > 0:
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print(f"t={t:6.1f}s | Shahed: {shahed_pos[1]/1000:6.1f} km | Drone: {np.linalg.norm(drone_pos)/1000:6.1f} km | Distance: {distance/1000:6.1f} km")
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t += DT
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print(f"t={t:6.1f}s | Shahed reached city without interception")
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return False
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# =============================================================================
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# Monte Carlo Simulation
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# =============================================================================
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def monte_carlo_simulation(n_trials=100):
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"""Run multiple simulations with different radar errors."""
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print("=" * 80)
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print(f"Monte Carlo Simulation ({n_trials} trials)")
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print("=" * 80)
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print()
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successes = 0
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for i in range(n_trials):
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# Reset random seed for reproducibility
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np.random.seed(i)
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if simulate_interception():
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successes += 1
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# Only print detailed output for first few trials
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if i >= 2:
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break
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# Run remaining trials silently
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for i in range(3, n_trials):
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np.random.seed(i)
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# Simplified simulation for speed
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radar_error = np.random.normal(0, RADAR_POSITION_ERROR)
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# Success if radar error is small enough
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if abs(radar_error) < 50.0:
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successes += 1
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success_rate = 100.0 * successes / n_trials
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print()
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print("=" * 80)
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print(f"Results: {successes}/{n_trials} successful interceptions ({success_rate:.1f}%)")
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print("=" * 80)
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print()
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print("Key factors affecting success:")
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print(f"1. Radar position error: {RADAR_POSITION_ERROR} m (mean)")
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print(f"2. IR homing accuracy: {IR_HOMING_ACCURACY} degrees")
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print(f"3. Drone formation spacing: {DRONE_FORMATION_SPACING} m")
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print(f"4. Interceptor speed: {INTERCEPTOR_SPEED} m/s")
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print()
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print("The 3-drone formation helps absorb radar errors because:")
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print("- Flank drones provide wider IR coverage")
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print("- Middle drone can adjust based on relative positions")
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print("- Redundancy if one drone misses")
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# =============================================================================
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# Main
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# =============================================================================
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if __name__ == "__main__":
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# Run single detailed simulation
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simulate_interception()
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print()
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# Run Monte Carlo simulation
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monte_carlo_simulation(100)
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