Digantara: Space Operations & Traffic Management

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MAP

Decision-ready analytics & modelling platform for mission assurance, planning, & operations.

OVERVIEW

From launch
to legacy

The MAP suite of analytics leverages the power of AIRA to help satellite operators plan, protect, and optimise their missions. From launch to de-orbit, it delivers the insights needed to avoid risks, stay on course, and make confident decisions at every step of the mission lifecycle.

What MAP delivers

COMPLETE MISSION ASSURANCE

Mission Design & Simulation

Plan and stress-test missions in a controlled digital environment, where satellite operations and orbital scenarios can be modelled, analysed, and optimised before real-world execution.

Launch & Early Operations

Covers the initial first phase after launch to help operators locate their satellites, establish contact, acquire their orbits, assess system health, and execute early maneuvers to ensure a smooth transition into mission operations.

In-orbit Operations & Support

Once a mission is underway, precise navigation and orbit management become critical. This includes satellite health monitoring, orbit determination and propagation, station-keeping, pass prediction, GNSS-based state estimation, orbit phasing, attitude and re-entry prediction to ensure stability and control throughout the mission.

Collision Avoidance & Manoeuvers

When collision risks arise, rapid screening and timely evasive planning keep missions safe. This includes monitoring for debris, risk assessment, and the execution of avoidance maneuvers or end-of-life strategies to protect assets and ensure mission continuity.

Rendezvous & Proximity Ops (RPO)

Guide a satellite to approach, dock, or inspect another satellite with precision navigation and real-time control, enabling safe and reliable proximity operations.

Why MAP stands apart

Fast & timely insights

Critical insights like conjunction alerts delivered < 15 secs and manoeuver < 4 mins.

Defence-grade analytics

Mission-ready capabilities designed for uncompromising reliability and security.

Precise predictions

40% more accurate tracking predictions compared to traditional systems.

Granular detection

Detect objects as small as 3cm, tracking 20x more objects.

Scalable across missions

Supports every mission profile, from single satellite launches to complex constellations.

Operational autonomy

Supports mission automation, anomaly response, and predictive guidance for complex tasks.

How MAP operates

Tailored Access

Data delivered as custom on-demand reports, through an API, or accessed via a dedicated interface.

Seamless Integration

Seamlessly integrates into your Common Operating Picture (COP), mission planning platforms, or any custom data dashboards.

End-to-End Security

Air-gapped on-prem deployment ensuring complete protection in high-security settings.

MAP in action

Anomaly Detection

Satellite Health Monitoring

Collision Risk Assesment

Testing Strategies & Ops

Risk Mitigation

In-orbit Servicing

Maneuver Optimisation

INNOVATION HUB

Pioneering discoveries

Multi-objective Multi-perspective Numerical Optimization of Collision Avoidance Maneuvers Using Differential Evolution

The design of collision avoidance maneuvers in real case scenarios involves intricate decision-making processes, demanding varying fidelity of data and processes at different stages. Mission constraints, propellant constraints, reliability of collision risk estimation, nature of secondary objects and even operator’s schedules contribute to the process of decision making. Therefore, it is imperative to adopt a multi-perspective approach to the problem formulation involving many (if not all) of the above-mentioned aspects. In this context, the maneuver design for collision avoidance is formulated as a heuristic multi-objective multi-perspective optimization problem in this research and the solution is obtained using Differential Evolution (DE), an evolutionary optimization technique. The objective functions to minimize in the problem formulation are a) mass of fuel used b) the collision probability after maneuver(s) c) the deviation of the maneuvered trajectory from the non-maneuvered nominal trajectory and d) disruption time of routine payload operations (defined as the time span for which the spacecraft deviates from its nominal orbit).

Re-entry Assessment Module: Protecting Terrestrial Assets from Space-based Threats

This white paper elucidates Digantara’s capability to mitigate risks to human life and property posed by the resident space objects (RSOs). Digantara’s re-entry assessment module provides a comprehensive understanding of the re-entry trajectories of these RSOs right from identification to tracking and prediction of these objects to minimize potential impacts to human lives.

Effect of Space Weather on Orbit Predictions in LEO

This whitepaper explores how variations in neutral density, driven by space weather, impact the orbit prediction of Resident Space Objects (RSOs) in Low-Earth Orbit (LEO). It evaluates the accuracy of Digantara’s proprietary orbit propagator, OrEng, through a testing framework described in the paper. Four atmospheric neutral density models - NRLMSISE-00, NRLMSIS2.0, JB2008, and WAM-IPE - were employed to assess OrEng's performance across a spectrum of Space Weather events.