Space Shuttle Navigation: Difference between revisions

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The numerical computation of the state vector can also be done if forces are acting, but the forces need to be known. [https://en.wikipedia.org/wiki/Inertial_measurement_unit <b>Inertial measurement units</b>] (IMUs), aboard the Shuttle, based on gyroscopes, sense any acceleration away from an inertial frame. Similarly functioning <b>Rate Gyro Assemblies</b> sense any changes in inertial attitude.
The numerical computation of the state vector can also be done if forces are acting, but the forces need to be known. [https://en.wikipedia.org/wiki/Inertial_measurement_unit <b>Inertial measurement units</b>] (IMUs), aboard the Shuttle, based on gyroscopes, sense any acceleration away from an inertial frame. Similarly functioning <b>Rate Gyro Assemblies</b> 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 be 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.
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. That's called the <b>propagated state vector</b>. 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 be 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 [https://en.wikipedia.org/wiki/Kalman_filter <b>Kalman filtering</b>], 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 <b>residual</b> (difference between direct measurement and same quantity computed from the state vector) and a <b>ratio</b> (likelihood of the sensor reading being good given the overall picture).  
Sensor information is always incorporated via [https://en.wikipedia.org/wiki/Kalman_filter <b>Kalman filtering</b>], 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 <b>residual</b> (difference between direct measurement and same quantity computed from the state vector) and a <b>ratio</b> (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.
It's important to note that a small residual does <i>not</i> mean that the propagated state vector is close to reality - it just means that the propagated state vector is close to a sensor reading, but they may both be wrong the same way. In particular, there are state vector update procedures which allow you to replace the propagated by the filtered state vector. After executing such a command, the residuals will always be small, but the state vector will be wrong if the filtered state vector was bad. It's important to understand this and make good judgements.
 
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. However, it is possible to switch to perfect navigation in which the propagated state vector equals the true state vector using the simulation options.


== Orbital  Navigation ==
== Orbital  Navigation ==

Revision as of 12:13, 24 March 2016

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This article or section will be worked on in the upcoming hours or days.
See history for the latest developments.

The navigational needs of the Space 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. That's called the propagated state vector. 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 be 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).

It's important to note that a small residual does not mean that the propagated state vector is close to reality - it just means that the propagated state vector is close to a sensor reading, but they may both be wrong the same way. In particular, there are state vector update procedures which allow you to replace the propagated by the filtered state vector. After executing such a command, the residuals will always be small, but the state vector will be wrong if the filtered state vector was bad. It's important to understand this and make good judgements.

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. However, it is possible to switch to perfect navigation in which the propagated state vector equals the true state vector using the simulation options.

Orbital Navigation

In a stable orbit, attitude drift (relevant for e.g. antenna pointing) is usually more important than position drift. For attitude sensing, the Shuttle is equipped with two star trackers, augmented by the Crew Optical Alignment System (COAS). Position updates to the state vector come either from the GPS receivers or from remote-sensing the Shuttle via radar whenever it overflies a ground site and uplinking state vector corrections determined that way.

The star tracker

Hardware

The star tracker system consists of two cameras, a left-pointing (-Y) and and upward pointing (-Z) system. Each of these has about a 10 deg field of view. If the star tracker observes a star within its visual field, it utilizes a list of ~100 bright stars stored in the data base to compare the coordinates in the sky using the current state vector with the coordinates the star should have from the data base. Measuring two stars that way gives an accurate update of the inertial attitude.

However, stars can only be identified by their position, so if the angular difference of internal and true state vector exceeds 1.2 deg, the star tracker can no longer operate - in this case the COAS has to be used to correct attitude.

Part of the orbital arrival procedure is to open the star tracker doors (which need to be closed for de-orbiting) - once that has done, the star tracker is operational.

Usage in FG

In orbit, the star tracker is controlled via SPEC 22.

STRK/COAS display of the Space Shuttle

During normal operations, items 3 and 4 (STAR TRK) should be selected. Items 5 or 6 allow one camera to be used for proximity operation navigation instead (see there), whereas items 9 and 10 switch the tracker off.

Whether a star is seen by the tracker or not is indicated following S PRES with a '*', the ID of the star is given as TRK ID, the angular correction to the current state vector as ΔANG. The upper right section shows a list of the three latest observations which can be cleared with item 20.

If a star tracker happens to point at the sun, shutters will automatically close to protect the optics, in which case shutter status will change from 'OP' to 'CL'. Whenever the Shuttle maneuvers, the angular motion of the cameras across the sky will be too fast to allow tracking and the status of the trackers will change to 'HI RATE'. If the star trackers go out of alignment and can no longer reliably identify stars, the status will change to 'FALSE TRK'.

Usually the star tracker should require minimal intervention and perform its function fine on its own.