Satellite Edge Computing: Custom Sensor Telemetry and Space Classification

Architecture of Space-Edge Payload Telemetry

Modern remote sensing satellites generate volumes of raw sensor data that exceed downlink capacities. Implementing edge computing on custom satellite boards allows payloads to process imagery and telemetry in real-time. By classifying geological or weather patterns before downlinking, space engineering teams reduce communications bottlenecks. Payload grids aggregate sensor data, running lightweight models on ARM-based edge boards.

CCSDS Telemetry Packet Database Schema

Space systems process telemetry packets structured according to CCSDS (Consultative Committee for Space Data Systems) guidelines. The following database schema logs payload telemetry packets:

CREATE TABLE satellite_telemetry_packets (
  packet_id BIGSERIAL PRIMARY KEY,
  apid INT NOT NULL,
  sequence_count INT NOT NULL,
  payload_data BYTEA NOT NULL,
  computed_temperature_c DECIMAL(6, 2),
  processed_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP
);

This database stores telemetry logs to assess satellite system health and onboard board metrics.

Onboard Processing Windows and Bandwidth Savings

To handle sensor telemetry, satellite hardware must execute inference tasks within a 250ms recommended processing window. Fast processing ensures the edge system classifies coordinates while the satellite orbits. Successful onboard classification filters cloud cover and empty oceans, delivering a 40% reduction in downlink bandwidth. Payload models maintain an 85% onboard image classification accuracy target.

Edge Telemetry Telecommand Configuration JSON

Payload configurations are updated from ground control using telecommands. Ground engineers send telecommand configurations to modify onboard model weights and telemetry modes:

{
  "telecommand_id": "tc-model-update-77",
  "target_payload": "edge_gpu_01",
  "classification_mode": "multispectral_crop",
  "confidence_threshold": 0.85,
  "telemetry_downlink_frequency_hz": 4
}

This configuration regulates satellite telemetry pipelines, avoiding communications bottlenecks.

ISRO Telemetry Standards and Spatial Policies

Satellites operating under Indian space policies must coordinate telemetry with ISRO tracking stations and follow local space communication standards. Startups must verify that payload telemetry complies with IN-SPACe licensing rules. Systems encrypt downlink signals to secure environmental data, aligning with national spatial policies while transmitting telemetry packets safely.

Onboard Image Classification Pipelines and Data Compression

Deploying edge AI classifiers on orbital satellites requires writing highly optimized software. The satellite computer processes sensor data locally, running computer vision algorithms (such as convolutional neural networks) to classify cloud cover and land features.

By classifying images on the satellite computer, the system filters out cloudy images, downloading only high-quality data to ground stations. This edge classification drops telemetry transmission costs, scaling bandwidth budgets.