Pattern Detection

Detect unusual behavior, maneuvers, and anomalies in satellite operations using AI-powered pattern analysis.

Status: Early Access — Request API key

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 RotaStellarClient

client = RotaStellarClient(api_key="rs_...")

# Get detected patterns for a satellite
patterns = client.list_patterns(
    satellite_id="44832",  # 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()

import { RotaStellarClient } from '@rotastellar/sdk';

const client = new RotaStellarClient({ apiKey: 'rs_...' });

const patterns = await client.listPatterns({
  satelliteId: '44832',  // COSMOS-2542
  lookbackDays: 30
});

for (const pattern of patterns) {
  console.log(`${pattern.type}: ${pattern.description}`);
  console.log(`Confidence: ${pattern.confidence}`);
}

curl "https://api.rotastellar.com/v1/patterns?satellite=COSMOS-2542&lookback_days=30" \
  -H "Authorization: Bearer rs_your_api_key"

Get Patterns

GET /v1/patterns

string

required

Satellite ID or name

integer

default:"30"

Analysis window (1-365 days)

string

Filter by pattern type: maneuver, anomaly, proximity, operational

number

default:"0.7"

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.list_patterns(
    satellite_id="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.list_patterns(
    satellite_id="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.list_patterns(
    satellite_id="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 via webhook
# See /intelligence/webhooks for full webhook setup
import requests

requests.post(
    "https://api.rotastellar.com/v1/patterns/monitor",
    headers={"Authorization": "Bearer rs_your_api_key"},
    json={
        "satellites": ["CRITICAL-SAT-1", "CRITICAL-SAT-2"],
        "types": ["anomaly", "proximity"],
        "min_confidence": 0.8,
        "webhook_url": "https://your-app.com/pattern-alerts"
    }
)

curl -X POST https://api.rotastellar.com/v1/patterns/monitor \
  -H "Authorization: Bearer rs_your_api_key" \
  -H "Content-Type: application/json" \
  -d '{
    "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.list_patterns(
    satellite_id="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
satellites = ["SAT-001", "SAT-002", "SAT-003"]
results = {}

for sat_id in satellites:
    patterns = client.list_patterns(satellite_id=sat_id, lookback_days=30)
    results[sat_id] = patterns

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")

Getting Started

Planning Tools

Orbital Intelligence

CAE

Operator Agent

Mission Control

Orbital Runtime

Orbital Sim

Distributed Compute

SDKs

Resources

Pattern Detection

Pattern Detection

Overview

Quick Start

Get Patterns

Response

Pattern Types

Maneuvers

Anomalies

Proximity Operations

Real-Time Anomaly Detection

Historical Analysis

Batch Analysis

Next Steps

Webhooks

Satellite Tracking

​Pattern Detection

​Overview

​Quick Start

​Get Patterns

​Response

​Pattern Types

​Maneuvers

​Anomalies

​Proximity Operations

​Real-Time Anomaly Detection

​Historical Analysis

​Batch Analysis

​Next Steps

Webhooks

Satellite Tracking

Pattern Detection

Overview

Quick Start

Get Patterns

Response

Pattern Types

Maneuvers

Anomalies

Proximity Operations

Real-Time Anomaly Detection

Historical Analysis

Batch Analysis

Next Steps