Pattern Detection
Detect unusual behavior, maneuvers, and anomalies in satellite operations using AI-powered pattern analysis.
Overview
Pattern detection identifies:
- Maneuvers — Orbit-raising, lowering, plane changes
- Anomalies — Unexpected behavior deviations
- Operational changes — Mode changes, activation/deactivation
- Proximity operations — Rendezvous and docking
Quick Start
from rotastellar import RotaStellar
client = RotaStellar(api_key="rs_...")
# Get detected patterns for a satellite
patterns = client.intelligence.patterns(
satellite="COSMOS-2542",
lookback_days=30
)
for pattern in patterns:
print(f"Type: {pattern.type}")
print(f"Time: {pattern.timestamp}")
print(f"Confidence: {pattern.confidence}")
print(f"Description: {pattern.description}")
print()
Get Patterns
Analysis window (1-365 days)
Filter by pattern type: maneuver, anomaly, proximity, operational
Minimum confidence threshold (0-1)
Response
{
"satellite": {
"id": "44832",
"name": "COSMOS-2542"
},
"patterns": [
{
"id": "pat_xyz789",
"type": "maneuver",
"subtype": "orbit_raise",
"timestamp": "2026-01-15T08:23:00Z",
"confidence": 0.95,
"description": "Orbit raising maneuver detected, altitude increased by 12km",
"details": {
"delta_altitude_km": 12.3,
"delta_v_estimated_m_s": 2.1,
"pre_altitude_km": 538,
"post_altitude_km": 550.3
}
},
{
"id": "pat_abc456",
"type": "proximity",
"subtype": "approach",
"timestamp": "2026-01-10T14:45:00Z",
"confidence": 0.88,
"description": "Approached USA-245 within 50km, maintained position for 6 hours",
"details": {
"target": "USA-245",
"min_distance_km": 48.2,
"duration_hours": 6.2
}
},
{
"id": "pat_def123",
"type": "anomaly",
"subtype": "attitude_change",
"timestamp": "2026-01-05T22:10:00Z",
"confidence": 0.72,
"description": "Unusual attitude variation detected, possible sensor reorientation",
"details": {
"magnitude_deg": 15.3,
"duration_min": 45
}
}
],
"analysis_window": {
"start": "2025-12-22T00:00:00Z",
"end": "2026-01-21T00:00:00Z"
}
}
Pattern Types
Maneuvers
Detected orbital changes:
| Subtype | Description |
|---|
orbit_raise | Altitude increase |
orbit_lower | Altitude decrease |
plane_change | Inclination adjustment |
phasing | Along-track position adjustment |
station_keeping | Maintenance maneuver |
deorbit | End-of-life maneuver |
# Get only maneuvers
maneuvers = client.intelligence.patterns(
satellite="STARLINK-1234",
type="maneuver",
lookback_days=90
)
for m in maneuvers:
print(f"{m.timestamp}: {m.subtype}")
print(f" Delta-V: {m.details.delta_v_estimated_m_s} m/s")
Anomalies
Unexpected behavior deviations:
| Subtype | Description |
|---|
attitude_change | Unexpected orientation change |
tumbling | Loss of attitude control |
fragmentation | Debris generation event |
signal_loss | Communication anomaly |
trajectory_deviation | Unexpected position change |
# Get anomalies
anomalies = client.intelligence.patterns(
satellite="DEBRIS-12345",
type="anomaly",
lookback_days=7
)
for a in anomalies:
if a.subtype == "fragmentation":
print(f"ALERT: Possible fragmentation at {a.timestamp}")
Proximity Operations
Close approaches and rendezvous:
# Detect proximity operations
proximity = client.intelligence.patterns(
satellite="INSPECTOR-SAT",
type="proximity",
lookback_days=60
)
for p in proximity:
print(f"Approached {p.details.target}")
print(f" Min distance: {p.details.min_distance_km} km")
print(f" Duration: {p.details.duration_hours} hours")
Real-Time Anomaly Detection
Get immediate alerts for anomalies:
# Set up anomaly monitoring
monitor = client.intelligence.patterns.monitor(
satellites=["CRITICAL-SAT-1", "CRITICAL-SAT-2"],
types=["anomaly", "proximity"],
min_confidence=0.8,
webhook_url="https://your-app.com/pattern-alerts"
)
Historical Analysis
Analyze long-term behavioral patterns:
# Get 1-year pattern history
history = client.intelligence.patterns(
satellite="GEO-SAT-1",
lookback_days=365
)
# Analyze maneuver frequency
maneuvers = [p for p in history if p.type == "maneuver"]
print(f"Total maneuvers: {len(maneuvers)}")
print(f"Station-keeping: {sum(1 for m in maneuvers if m.subtype == 'station_keeping')}")
Batch Analysis
Analyze patterns across multiple satellites:
# Analyze entire constellation
results = client.intelligence.patterns.batch(
satellites=["SAT-001", "SAT-002", "SAT-003"],
lookback_days=30
)
for sat_id, patterns in results.items():
anomalies = [p for p in patterns if p.type == "anomaly"]
if anomalies:
print(f"{sat_id}: {len(anomalies)} anomalies detected")
Next Steps
