Collision Risk Management

Collision Threat Characterization

Quantifying the collision threat consists of computing the collision probability, estimating how the collision probability will evolve, and determining a confidence factor for the corresponding probability calculations. SpaceNav’s collision threat characterization analysis software consists of the following tools:

1. Collision Probability Analysis
The Collision Probability Analysis utility computes the collision probability via 2D and 3D numerical integration methods. The 2D method assumes rectilinear relative motion and thus performs the numerical integration at the time of closest approach. The 3D method computes the probability by accumulated collision probability over some user-defined period of time. The 3D method is mostly used to perform collision risk analysis for low-speed and formation flying conjunction events. Output from the collision probability analysis utility consists of various figures and text reports.

Collision Probability

2. Monte Carlo Analysis
The Monte Carlo Analysis utility computes the collision probability via Monte Carlo simulation. Additionally analysis includes an estimate of how the collision probability will evolve as the time to the event is reduced. SpaceNav’s forecasting methodology uses the expected change in the objects’ state and state uncertainty information to forecast the collision risk at a given time in the future.

Collision Probability Forecasting

3. Conjunction Event Trending
The Conjunction Event Trending utility generates a graphical time history of various conjunction event parameters. The tool is used to establish behavior of the conjunction event as the time to the close approach point is reduced.

Position Uncertainty Evolution

4. Current Status & Action Report
The Current Status & Report utility generates a summary status for all active conjunction events. For each event, a current threat category is determined by a pre-defined set of rules. An action statement describing the ‘next steps’ is provided as well.

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