Automate sensor tasking without manual scheduling bottlenecks

Space domain awareness (SDA) tracks satellites, debris, and potential threats through coordinated sensor observations maintaining accurate catalogs of space objects. Manual sensor scheduling cannot keep pace with tens of thousands of objects requiring continuous tracking across limited sensor resources. Automated sensor tasking uses artificial intelligence to optimize observation schedules coordinating government and commercial sensors, ensuring critical objects get tracked while maintaining comprehensive catalog coverage. Heimdall automates this scheduling for US Space Force SSA/SDA operations, eliminating manual coordination delays that create dangerous gaps in space surveillance. Growing orbital congestion makes automation essential—human schedulers cannot optimize thousands of objects across dozens of sensors fast enough.

AI-automated sensor tasking evaluates thousands of potential observation opportunities simultaneously, selecting optimal sensor-object pairings based on orbital geometry, sensor availability, weather, priorities, and coverage gaps. Operators define tracking priorities and constraints while machine learning algorithms compute deconflicted schedules in seconds. The system continuously monitors execution and dynamically reschedules observations when weather changes, sensors fail, or urgent targets emerge. Dashboard provides real-time visibility into planned observations, sensor utilization, and catalog coverage across the entire network. This approach maintains comprehensive space domain awareness impossible through manual scheduling—spreadsheets and email coordination cannot adapt minute-by-minute to changing conditions while optimizing across competing priorities.

Automated tasking systems coordinate ground-based optical telescopes, ground-based radars, and space-based sensors across government and commercial networks. Each sensor type has different capabilities—optical telescopes track large satellites at night in clear weather, radars detect small debris regardless of daylight or clouds, space-based sensors provide continuous coverage from orbit. Heimdall integrates Space Force Space Surveillance Network sensors with commercial providers and international partners through a unified scheduling platform. The system understands each sensor's characteristics including field of view, detection limits, observation cadence, and operational constraints. Automated coordination eliminates stovepipe operations where each sensor network schedules independently, maximizing total observation capability through integrated tasking that manual processes cannot achieve efficiently.

Automated systems treat commercial and government sensors as a unified network rather than separate stovepipes operating independently. Government sensors provide baseline surveillance coverage while commercial providers augment capacity with additional observation opportunities. Unified scheduling platform coordinates across both networks eliminating duplicate observations of same objects while filling coverage gaps neither network handles alone. Heimdall integrates government and commercial sensor networks for Space Force operations, leveraging commercial capacity augmenting the Space Surveillance Network without requiring manual coordination between organizations. This integration maximizes total observation capability—commercial sensors track routine catalog objects freeing government assets for high-priority threats requiring specialized capabilities. Automated deconfliction ensures observations complement rather than duplicate each other.

Catalog maintenance tracks known space objects with sufficient observation frequency to maintain accurate orbital predictions preventing lost objects. Each object requires periodic observations based on orbit uncertainty—high-value satellites need daily tracking while routine debris might need weekly observations. Manual scheduling limits catalog maintenance to a small fraction of total objects because human schedulers cannot optimize thousands of tracking requirements across limited sensor time. The growing space object population (tens of thousands currently) exceeds manual scheduling capacity. Automated scheduling maintains comprehensive catalog coverage by dynamically prioritizing observations based on orbit uncertainty, object value, and available sensor opportunities. Objects get observed when needed most rather than when schedulers manually process requests days later.

Automated systems insert high-priority observations immediately through dynamic rescheduling rather than requiring manual coordination across sensor networks. When urgent targets emerge—potential collisions, suspicious maneuvers, national security events—the system automatically adjusts scheduled observations accommodating priority changes within minutes. Dynamic rescheduling maintains maximum catalog coverage while satisfying urgent requests, something manual coordination cannot achieve at operational tempo. Heimdall provides this capability for Space Force SSA/SDA operations where urgent tasking previously required email chains and phone calls taking hours or days. Real-time adaptation ensures critical observations happen when needed rather than waiting for the next manual scheduling cycle. The system optimizes trade-offs between routine catalog maintenance and urgent requirements automatically.

Yes. Automated systems select optimal sensors for each observation based on object characteristics, orbital conditions, and sensor availability. Optical telescopes excel at tracking large satellites in GEO orbits during nighttime clear weather. Ground radars detect small LEO debris regardless of daylight or clouds. Space-based sensors provide continuous coverage from orbit. Manual schedulers cannot compute best sensor-object pairings across these variables at scale. Machine learning evaluates sensor capabilities against object requirements selecting combinations maximizing detection probability and catalog maintenance efficiency. This multi-sensor optimization is the primary advantage over manual approaches—AI evaluates thousands of permutations instantaneously while human schedulers default to familiar sensors regardless of whether alternatives would perform better.

US Space Force uses Heimdall for SSA/SDA sensor tasking optimization improving space object catalog maintenance. Heimdall provides a dashboard for real-time awareness of planned observations and related metrics, computes observation opportunities and scoring, manages configurable sensors and sensor attributes, and integrates government and commercial sensor networks. DARC program demonstrates multi-array sensor tasking solution exceeding Space Force performance requirements for missile tracking, validating AI optimization approach. NOAA's EASI system schedules across NASA, USSF, NOAA, and commercial communication networks leveraging the same Astro Scheduler foundation adapted for sensor coordination. These operational deployments prove automated tasking at scale where manual scheduling cannot maintain comprehensive awareness of a congested orbital environment.

SpyMeSat is a mobile and web application enabling users to order commercial satellite imagery on-demand from multiple providers through a single interface. Traditional satellite imagery procurement requires navigating separate commercial vendors, understanding coverage capabilities, and waiting days or weeks for delivery. SpyMeSat simplifies access providing immediate ordering from smartphone or computer with imagery delivered in hours rather than days. Government and commercial customers use SpyMeSat for tactical planning, damage assessment, intelligence analysis, and mission support requiring rapid imagery access. The application integrates multiple commercial satellite imagery providers giving users choice of sensors, resolution, and collection timing. The on-demand model eliminates procurement delays enabling responsive intelligence gathering.

Automated systems integrate with existing Space Surveillance Network infrastructure through standard interfaces and open APIs rather than requiring wholesale replacement. Heimdall connects to sensor control systems receiving telemetry and status information while sending observation commands. System interfaces with catalog databases retrieving orbital predictions and uploading observation results. Dashboard provides operators visibility and control without replacing existing command and control systems. This integration approach enables gradual adoption—organizations modernize scheduling coordination while preserving operational sensor infrastructure and workflows. Open architecture allows connection to diverse sensor types and control systems without vendor lock-in. Automated scheduling layer coordinates observations across infrastructure without requiring sensors themselves to change, reducing deployment risk while improving coordination efficiency.