from typing import Tuple, List
from scipy.interpolate import griddata

class TerrainReferencedNavigation:
 def __init__(self, dem: np.ndarray, dem_resolution: float = 10.0):
 self.dem = dem
 self.resolution = dem_resolution
 self.dem_shape = dem.shape
 
 # Create coordinate grids
 self.x_grid = np.arange(dem.shape[1]) * dem_resolution
 self.y_grid = np.arange(dem.shape[0]) * dem_resolution
 
 def get_terrain_height(self, x: float, y: float) -> float:
 """Get terrain height at position (x, y)."""
 # Convert to grid indices
 ix = int(x / self.resolution)
 iy = int(y / self.resolution)
 
 if 0 <= ix < self.dem_shape[1] and 0 <= iy < self.dem_shape[0]:
 return self.dem[iy, ix]
 return 0.0
 
 def correlate_profile(self, measured_profile: np.ndarray,
 drone_position: np.ndarray,
 search_radius: float = 100.0) -> Tuple[np.ndarray, float]:
 """Find best matching position using profile correlation."""
 best_match = drone_position.copy()
 best_score = -float('inf')
 
 # Search around current position
 for dx in np.arange(-search_radius, search_radius, self.resolution):
 for dy in np.arange(-search_radius, search_radius, self.resolution):
 test_pos = drone_position + np.array([dx, dy, 0])
 
 # Extract DEM profile along same path
 dem_profile = self._extract_profile(test_pos, len(measured_profile))
 
 # Compute correlation
 if len(dem_profile) == len(measured_profile):
 score = np.corrcoef(measured_profile, dem_profile)[0, 1]
 
 if score > best_score:
 best_score = score
 best_match = test_pos[:2]
 
 return best_match, best_score
 
 def _extract_profile(self, position: np.ndarray, n_points: int) -> np.ndarray:
 """Extract terrain profile along flight direction."""
 profile = np.zeros(n_points)
 
 for i in range(n_points):
 x = position[0] + i * self.resolution
 y = position[1]
 profile[i] = self.get_terrain_height(x, y)
 
 return profile
 
 def TERCOM_algorithm(self, altimeter_readings: np.ndarray,
 flight_path: np.ndarray) -> np.ndarray:
 """Terrain Contour Matching (TERCOM) algorithm."""
 n_windows = len(altimeter_readings) - 10 # Window size
 
 best_position = flight_path[-1].copy()
 best_distance = float('inf')
 
 for i in range(0, len(flight_path) - n_windows):
 # Extract DEM section
 dem_section = []
 for j in range(n_windows):
 pos = flight_path[i + j]
 dem_section.append(self.get_terrain_height(pos[0], pos[1]))
 
 dem_section = np.array(dem_section)
 
 # Compute mean absolute difference
 if len(dem_section) == len(altimeter_readings):
 distance = np.mean(np.abs(altimeter_readings - dem_section))
 
 if distance < best_distance:
 best_distance = distance
 best_position = flight_path[i + n_windows // 2].copy()
 
 return best_position

class SandiaTerrainAlgorithm:
 """SANDIA terrain matching algorithm with DEM smoothing."""
 
 def __init__(self, dem: np.ndarray, smoothing_sigma: float = 2.0):
 from scipy.ndimage import gaussian_filter
 
 self.original_dem = dem
 self.smoothed_dem = gaussian_filter(dem, sigma=smoothing_sigma)
 self.smoothed_dem = dem # Use original for simplicity
 
 def match(self, altitude_profile: np.ndarray,
 initial_estimate: np.ndarray) -> np.ndarray:
 """Perform terrain matching with confidence estimation."""
 
 # Normalize altitude profile
 profile_mean = np.mean(altitude_profile)
 profile_std = np.std(altitude_profile)
 
 if profile_std > 0:
 normalized_profile = (altitude_profile - profile_mean) / profile_std
 else:
 return initial_estimate
 
 # Simple correlation-based matching
 # In practice, use more sophisticated methods
 best_position = initial_estimate[:2].copy()
 
 return best_position

from typing import Tuple, List

class MagneticNavigation:
 def __init__(self, magnetic_map: np.ndarray, map_resolution: float = 100.0):
 self.magnetic_map = magnetic_map
 self.resolution = map_resolution
 
 # IGRF reference model parameters
 self.igrf_coefficients = self._load_igrf_coefficients()
 
 def _load_igrf_coefficients(self):
 """Load International Geomagnetic Reference Field coefficients."""
 # Simplified - in practice use full IGRF model
 return {
 'g10': -29404.8, 'g11': -1450.9, 'h11': 4653.2,
 'g20': -2500.0, 'g21': 3010.0, 'h21': -2991.6
 }
 
 def compute_reference_field(self, position: np.ndarray) -> np.ndarray:
 """Compute reference magnetic field at position."""
 lat, lon, alt = position
 
 # Simplified dipole model
 # In practice, use full IGRF spherical harmonic expansion
 declination = 0.0 # Would come from IGRF
 inclination = 60.0 # Would come from IGRF
 intensity = 50000.0 # nT, would come from IGRF
 
 return np.array([
 intensity * np.cos(np.radians(inclination)) * np.cos(np.radians(declination)),
 intensity * np.cos(np.radians(inclination)) * np.sin(np.radians(declination)),
 intensity * np.sin(np.radians(inclination))
 ])
 
 def match_position(self, measured_field: np.ndarray,
 search_area: Tuple[np.ndarray, np.ndarray]) -> np.ndarray:
 """Find position that best matches magnetic measurements."""
 min_pos, max_pos = search_area
 
 best_position = (min_pos + max_pos) / 2
 best_error = float('inf')
 
 step = 100.0 # meters
 
 x = min_pos[0]
 while x <= max_pos[0]:
 y = min_pos[1]
 while y <= max_pos[1]:
 # Get map value
 map_value = self._get_map_value(x, y)
 
 if map_value is not None:
 error = np.linalg.norm(measured_field - map_value)
 
 if error < best_error:
 best_error = error
 best_position = np.array([x, y])
 
 y += step
 x += step
 
 return best_position
 
 def _get_map_value(self, x: float, y: float) -> np.ndarray:
 """Get magnetic value from map at position."""
 ix = int(x / self.resolution)
 iy = int(y / self.resolution)
 
 if 0 <= ix < self.magnetic_map.shape[1] and \
 0 <= iy < self.magnetic_map.shape[0]:
 return self.magnetic_map[iy, ix]
 return None
 
 def detect_anomaly(self, measured_field: np.ndarray,
 reference_field: np.ndarray) -> float:
 """Detect magnetic anomaly (difference from expected)."""
 anomaly = measured_field - reference_field
 return np.linalg.norm(anomaly)
 
 def kalman_filter_magnetic(self, measurements: List[np.ndarray],
 dt: float = 1.0) -> List[np.ndarray]:
 """Kalman filter for magnetic field estimation."""
 # State: [x, y, Bx, By, Bz]
 state = np.zeros(6)
 P = np.eye(6) * 100
 
 positions = []
 
 for z in measurements:
 # Predict (simple constant velocity)
 F = np.eye(6)
 F[0, 2] = dt
 F[1, 3] = dt
 
 state = F @ state
 P = F @ P @ F.T + np.eye(6) * 0.1
 
 # Update with magnetic measurement
 H = np.zeros((3, 6))
 H[0:3, 2:5] = np.eye(3)
 
 R = np.eye(3) * 100 # Measurement noise
 y = z - state[2:5]
 S = H @ P @ H.T + R
 K = P @ H.T @ np.linalg.inv(S)
 
 state = state + K @ y
 P = (np.eye(6) - K @ H) @ P
 
 positions.append(state[:2].copy())
 
 return positions

from datetime import datetime, timedelta
from typing import Tuple

class CelestialNavigation:
 def __init__(self):
 # Solar position constants
 self_speed = 360 / 365.25 # degrees per day
 
 def compute_sun_position(self, datetime_utc: datetime,
 latitude: float, longitude: float) -> Tuple[float, float]:
 """Compute sun azimuth and elevation."""
 # Day of year
 day_of_year = datetime_utc.timetuple().tm_yday
 
 # Solar declination
 declination = 23.45 * np.sin(np.radians(360/365 * (day_of_year - 81)))
 
 # Hour angle
 solar_noon = 12.0 # Approximate
 hours_since_noon = datetime_utc.hour + datetime_utc.minute/60 - solar_noon
 hour_angle = hours_since_noon * 15 # 15 degrees per hour
 
 # Solar elevation
 lat_rad = np.radians(latitude)
 dec_rad = np.radians(declination)
 ha_rad = np.radians(hour_angle)
 
 elevation = np.arcsin(
 np.sin(lat_rad) * np.sin(dec_rad) +
 np.cos(lat_rad) * np.cos(dec_rad) * np.cos(ha_rad)
 )
 
 # Solar azimuth
 azimuth = np.arctan2(
 -np.sin(ha_rad),
 np.tan(dec_rad) * np.cos(lat_rad) - np.sin(lat_rad) * np.cos(ha_rad)
 )
 
 return np.degrees(azimuth), np.degrees(elevation)
 
 def determine_heading_from_sun(self, datetime_utc: datetime,
 latitude: float, longitude: float,
 measured_elevation: float) -> float:
 """Determine heading from sun elevation measurement."""
 # Get expected sun position
 _, expected_elevation = self.compute_sun_position(
 datetime_utc, latitude, longitude
 )
 
 # Heading correction based on elevation difference
 # This is simplified - real implementation uses full spherical astronomy
 heading_correction = measured_elevation - expected_elevation
 
 return heading_correction
 
 def star_tracker_heading(self, star_positions: list,
 catalog_stars: dict) -> float:
 """Determine heading from star observations."""
 if len(star_positions) < 2:
 return None
 
 # Match observed stars to catalog
 matched_stars = []
 for obs_pos in star_positions:
 for star_id, catalog_pos in catalog_stars.items():
 angular_distance = self._angular_distance(obs_pos, catalog_pos)
 if angular_distance < np.radians(5): # 5 degree tolerance
 matched_stars.append((obs_pos, catalog_pos))
 break
 
 if len(matched_stars) < 2:
 return None
 
 # Compute rotation between observed and catalog frames
 # Using the two best matches
 obs1, cat1 = matched_stars[0]
 obs2, cat2 = matched_stars[1]
 
 # Compute angle differences
 obs_angle = np.arctan2(obs1[1] - obs2[1], obs1[0] - obs2[0])
 cat_angle = np.arctan2(cat1[1] - cat2[1], cat1[0] - cat2[0])
 
 heading = cat_angle - obs_angle
 
 return np.degrees(heading) % 360
 
 def _angular_distance(self, pos1, pos2):
 """Compute angular distance between two sky positions."""
 return np.arccos(np.clip(
 np.sin(pos1[1]) * np.sin(pos2[1]) +
 np.cos(pos1[1]) * np.cos(pos2[1]) * np.cos(pos1[0] - pos2[0]),
 -1, 1
 ))

class StarCatalog:
 """Manage star catalog for celestial navigation."""
 
 def __init__(self):
 self.bright_stars = {
 'Polaris': (np.radians(37.95), np.radians(89.26)),
 'Sirius': (np.radians(101.29), np.radians(-16.72)),
 'Canopus': (np.radians(95.99), np.radians(-52.70)),
 'Arcturus': (np.radians(213.92), np.radians(19.18)),
 'Vega': (np.radians(279.23), np.radians(38.78)),
 'Capella': (np.radians(79.17), np.radians(46.00)),
 'Rigel': (np.radians(78.63), np.radians(-8.20)),
 'Procyon': (np.radians(114.83), np.radians(5.23)),
 }
 
 def get_reference_stars(self, min_elevation: float = 30.0) -> dict:
 """Get stars suitable for navigation."""
 return self.bright_stars

from typing import Dict, List, Optional
from dataclasses import dataclass

@dataclass
class NavigationState:
 position: np.ndarray
 velocity: np.ndarray
 orientation: np.ndarray
 covariance: np.ndarray
 confidence: float

class GPSDeniedNavigationSystem:
 def __init__(self):
 self.vio = None # Visual-Inertial Odometry
 self.terrain_nav = None # Terrain navigation
 self.magnetic_nav = None # Magnetic navigation
 self.celestial_nav = CelestialNavigation()
 
 self.state = NavigationState(
 position=np.zeros(3),
 velocity=np.zeros(3),
 orientation=np.zeros(3),
 covariance=np.eye(6) * 1.0,
 confidence=1.0
 )
 
 self.available_sensors = set()
 self.sensor_weights = {
 'vio': 0.4,
 'terrain': 0.3,
 'magnetic': 0.2,
 'celestial': 0.1
 }
 
 def update_sensor_availability(self, sensors: Dict[str, bool]):
 """Update which sensors are currently available."""
 for sensor, available in sensors.items():
 if available:
 self.available_sensors.add(sensor)
 else:
 self.available_sensors.discard(sensor)
 
 def fuse_measurements(self, measurements: Dict[str, np.ndarray]) -> NavigationState:
 """Fuse measurements from available sensors."""
 positions = []
 weights = []
 
 # VIO
 if 'vio' in measurements and 'vio' in self.available_sensors:
 vio_pos = measurements['vio']
 positions.append(vio_pos)
 weights.append(self.sensor_weights['vio'])
 
 # Terrain
 if 'terrain' in measurements and 'terrain' in self.available_sensors:
 terrain_pos = measurements['terrain']
 positions.append(terrain_pos)
 weights.append(self.sensor_weights['terrain'])
 
 # Magnetic
 if 'magnetic' in measurements and 'magnetic' in self.available_sensors:
 magnetic_pos = measurements['magnetic']
 positions.append(magnetic_pos)
 weights.append(self.sensor_weights['magnetic'])
 
 # Celestial
 if 'celestial' in measurements and 'celestial' in self.available_sensors:
 celestial_pos = measurements['celestial']
 positions.append(celestial_pos)
 weights.append(self.sensor_weights['celestial'])
 
 if not positions:
 return self.state
 
 # Normalize weights
 weights = np.array(weights)
 weights = weights / weights.sum()
 
 # Weighted fusion
 positions = np.array(positions)
 fused_position = np.average(positions, axis=0, weights=weights)
 
 # Update covariance
 if len(positions) > 1:
 position_variance = np.var(positions, axis=0)
 self.state.covariance[:3, :3] = np.diag(position_variance)
 
 # Update state
 self.state.position = fused_position
 self.state.confidence = 1.0 - np.sqrt(np.mean(self.state.covariance[:3, :3]))
 
 return self.state
 
 def predict_position(self, dt: float, control_input: np.ndarray) -> np.ndarray:
 """Dead reckoning prediction when no measurements available."""
 # Simple constant velocity model
 self.state.position += self.state.velocity * dt
 self.state.velocity += control_input * dt
 
 # Increase uncertainty
 self.state.covariance += np.eye(6) * 0.1 * dt
 
 self.state.confidence *= 0.99 # Decay confidence
 
 return self.state.position.copy()
 
 def get_navigation_solution(self) -> Dict:
 """Get current navigation solution with quality metrics."""
 return {
 'position': self.state.position.copy(),
 'velocity': self.state.velocity.copy(),
 'orientation': self.state.orientation.copy(),
 'position_uncertainty': np.sqrt(np.diag(self.state.covariance[:3, :3])),
 'confidence': self.state.confidence,
 'available_sensors': list(self.available_sensors),
 'recommended_action': self._get_recommendation()
 }
 
 def _get_recommendation(self) -> str:
 """Recommend action based on navigation quality."""
 if self.state.confidence > 0.8:
 return 'NORMAL_OPERATION'
 elif self.state.confidence > 0.5:
 return 'REDUCE_SPEED'
 elif self.state.confidence > 0.3:
 return 'INCREASE_ALTITUDE'
 else:
 return 'CONSIDER_RTL'

from typing import Dict, List

class GPSDeniedDrone:
 def __init__(self, environment: str = 'indoor'):
 self.environment = environment
 
 # Navigation systems based on environment
 self.nav_system = GPSDeniedNavigationSystem()
 
 # Environment-specific configurations
 self.config = self._get_config(environment)
 
 # State
 self.position = np.zeros(3)
 self.velocity = np.zeros(3)
 self.heading = 0.0
 
 # Logs
 self.navigation_log = []
 
 def _get_config(self, environment: str) -> Dict:
 configs = {
 'indoor': {
 'sensors': ['vio', 'uwb', 'wifi'],
 'update_rate': 30,
 'max_speed': 2.0
 },
 'urban': {
 'sensors': ['vio', 'magnetic', 'visual_landmarks'],
 'update_rate': 10,
 'max_speed': 5.0
 },
 'cave': {
 'sensors': ['vio', 'magnetic'],
 'update_rate': 10,
 'max_speed': 1.0
 },
 'forest': {
 'sensors': ['vio', 'terrain', 'magnetic'],
 'update_rate': 5,
 'max_speed': 3.0
 }
 }
 return configs.get(environment, configs['indoor'])
 
 def process_sensor_data(self, sensor_data: Dict) -> Dict:
 """Process sensor data and update navigation."""
 measurements = {}
 
 # Process each available sensor
 for sensor in self.config['sensors']:
 if sensor in sensor_data:
 if sensor == 'vio':
 measurements['vio'] = self._process_vio(sensor_data[sensor])
 elif sensor == 'uwb':
 measurements['uwb'] = self._process_uwb(sensor_data[sensor])
 elif sensor == 'magnetic':
 measurements['magnetic'] = self._process_magnetic(sensor_data[sensor])
 elif sensor == 'terrain':
 measurements['terrain'] = self._process_terrain(sensor_data[sensor])
 
 # Fuse measurements
 if measurements:
 state = self.nav_system.fuse_measurements(measurements)
 self.position = state.position
 self.velocity = state.velocity
 
 # Log
 self.navigation_log.append({
 'position': self.position.copy(),
 'sensors_used': list(measurements.keys()),
 'confidence': self.nav_system.state.confidence
 })
 
 return {
 'position': self.position,
 'velocity': self.velocity,
 'heading': self.heading,
 'confidence': self.nav_system.state.confidence
 }
 
 def _process_vio(self, data: Dict) -> np.ndarray:
 """Process VIO data."""
 return data.get('position', self.position)
 
 def _process_uwb(self, data: Dict) -> np.ndarray:
 """Process UWB ranging data."""
 return data.get('position', self.position)
 
 def _process_magnetic(self, data: Dict) -> np.ndarray:
 """Process magnetic field data."""
 return data.get('position', self.position)
 
 def _process_terrain(self, data: Dict) -> np.ndarray:
 """Process terrain profile data."""
 return data.get('position', self.position)
 
 def navigate_to(self, target: np.ndarray) -> Dict:
 """Navigate to target in GPS-denied environment."""
 error = target - self.position
 distance = np.linalg.norm(error)
 
 if distance < 0.5:
 return {'status': 'arrived', 'position': self.position}
 
 # Velocity command
 speed = min(self.config['max_speed'], distance * 0.5)
 direction = error / distance
 velocity_cmd = direction * speed
 
 # Update velocity
 self.velocity = velocity_cmd
 
 return {
 'status': 'navigating',
 'velocity_command': velocity_cmd,
 'distance_to_target': distance,
 'position': self.position,
 'confidence': self.nav_system.state.confidence
 }
 
 def emergency_procedure(self) -> Dict:
 """Execute emergency procedure when navigation fails."""
 print("EMERGENCY: Navigation confidence critical!")
 
 # Try to find any available positioning
 available = self.nav_system.available_sensors
 
 if len(available) == 0:
 return {
 'action': 'HOVER_AND_AWAIT',
 'message': 'No positioning available - hovering'
 }
 
 # Land if possible
 return {
 'action': 'CONTROLLED_LANDING',
 'message': 'Attempting controlled landing'
 }
 
 def get_navigation_report(self) -> Dict:
 """Generate comprehensive navigation report."""
 report = {
 'current_position': self.position.tolist(),
 'heading': np.degrees(self.heading),
 'speed': np.linalg.norm(self.velocity),
 'confidence': self.nav_system.state.confidence,
 'sensors_active': list(self.nav_system.available_sensors),
 'position_uncertainty': np.sqrt(
 np.diag(self.nav_system.state.covariance[:3, :3])
 ).tolist(),
 'environment': self.environment,
 'navigation_log_length': len(self.navigation_log)
 }
 
 return report

def main():
 # Test GPS-denied navigation in different environments
 environments = ['indoor', 'urban', 'cave', 'forest']
 
 for env in environments:
 print(f"\n{'='*50}")
 print(f"Testing GPS-denied navigation: {env.upper()}")
 print('='*50)
 
 drone = GPSDeniedDrone(environment=env)
 
 # Simulate navigation
 target = np.array([10.0, 10.0, 2.0])
 
 for step in range(20):
 result = drone.navigate_to(target)
 
 # Simulate sensor data
 sensor_data = {
 'vio': {'position': drone.position + np.random.randn(3) * 0.1},
 }
 
 nav_state = drone.process_sensor_data(sensor_data)
 
 print(f"Step {step}: Position={nav_state['position'][:2]}, "
 f"Confidence={nav_state['confidence']:.2f}")
 
 if result['status'] == 'arrived':
 print("Target reached!")
 break
 
 # Print report
 report = drone.get_navigation_report()
 print(f"\nNavigation Report:")
 print(f" Final position: {report['current_position'][:2]}")
 print(f" Sensors used: {report['sensors_active']}")

if __name__ == "__main__":
 main()