Space Shuttle Navigation

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The navigational needs of theSpace Shuttle change substantially during the course of a mission. Once in a stable orbit, the position of the Shuttle at any given time can be predicted fairly accurately by simply solving the equations of orbital motion. This is different in the atmosphere where such predictions are much more involved because atmospheric flight is fairly complicated. Different techniques and different hardware is used for both cases.

State vector

The central concept of Space Shuttle navigation is the state vector of the vehicle. As the name states, it describes the state of the spacecraft at a given instance in time, i.e. it contains a timestamp, the three position coordinates (measured in an inertial coordinate frame), the three velocity vector components and the three attitude angles (yaw, pitch and roll).

Given the state vector, numerical orbital motion prediction can be used to determine the state vector at any future time if there are no forces acting on the Shuttle. Note that for a number of reasons standard Kepler 2-body orbital mechanics is not accurate enough for the purpose - in reality (and in FG) Earth is not a point mass but has a more complicated gravity field.

The numerical computation of the state vector can also be done if forces are acting, but the forces need to be known. Inertial measurement units (IMUs), aboard the Shuttle, based on gyroscopes, sense any acceleration away from an inertial frame. Similarly functioning Rate Gyro Assemblies sense any changes in inertial attitude.

The Space Shuttle Avionics hence knows where the Shuttle is at any time by computing the state vector numerically given orbital mechanics and the sensed forces. This procedure is error-prone, because neither are the equations of motion for the numerical prediction perfectly known, nor can they be computed to perfect accuracy, nor can the forces sensed with perfect accuracy. Over time, the internal state vector known to GNC will therefore deviate from the true state vector. The purpose of the navigation systems is to supply extra sensor information to minimize the drift and to periodically correct the state vector.

Sensor information is always incorporated via Kalman filtering, i.e. GNC estimates a likelihood that any sensor reading is 'real' given the internal state vector and incorporates new data only if it fits into the overall picture. In this way, navigation does not go astray in case of sensor failures. Given a filter, any sensor reading can be characterized by a residual (difference between direct measurement and same quantity computed from the state vector) and a ratio (likelihood of the sensor reading being good given the overall picture).

The basic characteristics of the navigational problem (including error propagation, sensor quality with respect to the propagated state, state vector corrections) are modeled in FG.

Space Navigation